Air Pollution, Externalities, and DecentralizedEnvironmental Regulation∗

Branko BoskovicUniversity of Alberta

This version: November 23, 2015

Abstract

This paper investigates the extent to which interjurisdictional spillovers – transbound-ary pollution and the so-called ‘race to the bottom’ – distort decentralized environ-mental regulation in practice. To that end, I develop a model in which local juris-dictions set regulations by trading-off air quality against attracting mobile firms. Inequilibrium, local regulations are interdependent, creating a challenging identificationproblem, which I address using exclusion restrictions derived from the physics of airpollutant transport. Specifically, for short-lived pollutants, transboundary pollution isregional instead of global, and exogenous factors that disperse pollution (such as persis-tent local wind patterns) can shift neighbors’ policies through transboundary pollutionbut not the policies of distant or upwind jurisdictions. I estimate the model usinga new panel dataset covering a unique institutional setting, namely the endogenousdecentralization of air pollution standards from the U.S. federal government to statesduring 1971-1990, along with ambient particulate matter concentrations, the locationof regulated industry, and weather patterns. I find that the transfer of regulatory au-thority to a state increases the number of firms there by 3%, decreases firms in nearbystates by nearly 2%, and increases air pollution in neighboring downwind states by 1%.The evidence implies that states seek regulatory authority to attract firms and exportpollution, thereby making decentralized regulation of air pollution inefficient.

Keywords: Decentralization, air pollution regulation, transboundary pollution, ‘raceto the bottom’JEL codes: H73, Q53, Q58

∗I thank Robert McMillan, Aloysius Siow and Matthew Turner for their guidance and support. I am alsograteful to Victor Aguirregabiria, Antonio Bento, Andrew Bird, David P. Byrne, Don Dewees, Gregory Evans,Marco Gonzalez-Navarro, Sacha Kapoor, Kory Kroft, Andrew Leach, Joshua Lewis, Hugh Macartney, ArvindMagesan, Michael Smart, Junichi Suzuki, and Laura Turner for comments, as well seminar participantsat Erasmus University, University of Alberta, Johns Hopkins University, University of Toronto, the 2011Canadian Economics Association Meetings, Camp Resources XVIII, the 1st Northeast Workshop in EnergyPolicy and Environmental Economics, and the 2013 Canadian Workshop in Environmental Economics andPolicy. All omissions and errors are my own. Contact: Alberta School of Business, University of Alberta,3-23 Business Building, Edmonton, Alberta T6G 2R6, Canada (email: boskovic@ualberta.ca).

1 Introduction

The relative merits of decentralization in a regulatory context have been the focus of a

considerable body of theoretical research as well as active policy debate. While local govern-

ments may be better able to tailor regulations to local tastes than central authorities, they

typically lack the incentive to internalize spillovers, whether they arise from transboundary

pollution or the competition for mobile factors associated with the so-called ‘race to the

bottom.’ As a result of both types of spillover, decentralized regulation – very widespread

in an environmental context – may generate inefficient outcomes.

These issues surrounding the choice of regulatory structure are particularly salient in the

realm of controlling air pollution. The production of air pollution is ubiquitous, pollution is

easily transported long distances, and it is very harmful to human health: urban air pollu-

tion alone causes 2.2% of all global premature deaths annually (World Health Organization

(2009)). Against such considerable health costs, there are concerns that stringent environ-

mental regulation will inhibit firm growth and cost jobs; such concerns are apparent, for

instance, in the strong opposition from some quarters to cap-and-trade.1 Lawmakers have

long been aware of such issues and have attempted to address them accordingly. In the

context of U.S. air pollution regulations – a prime example – Congress enacted the Clean

Air Act (CAA) in 1970 in order to protect citizens’ health from excessive air pollution,

in part because of a perception that individual states were inhibited from controlling pollu-

tion due to pressures to remain ‘competitive’ (see U.S.H.R. (1979)).2 More recently, the U.S.

promulgated the Cross-State Air Pollution Rule, forcing states to reduce interstate pollution.

Highly relevant to this ongoing policy discussion, a substantial theoretical literature has

analyzed the setting of environmental regulations in an interjurisdictional context.3 This lit-

erature has delivered clear and intuitive prescriptions concerning the way regulatory systems

should be structured efficiently, based on the sign and magnitude of three elasticities: (i)

the responsiveness of polluting industries to interjurisdictional policy differences; (ii) the ef-

fect that local policy and non-local policies, through transboundary spillovers, have on local

pollution; and (iii) the responsiveness of local policy to other governments’ policies because

of transboundary pollution and competition for mobile firms. Yet given the endogeneity

1The so-called Waxman-Markey bill died in the U.S. Senate in 2010 partly because it included a cap-and-trade scheme that was derisively referred to as ‘cap and tax’; see Broder (2010) for a description of itsdemise.

2Winning a policy competition over other jurisdications can be lucrative: Greenstone et al. (2010) findthat U.S. counties that win bids to attract large industrial plants experience significant economic benefits.

3See Cropper and Oates (1992), Oates (2002), and Oates and Portney (2003) for comprehensive surveys.

1

of regulation-setting and the difficulty in finding appropriate instruments, identifying and

measuring these elasticities has proved challenging empirically.

This paper seeks to address the empirical challenges by developing an approach to cred-

ibly identify these three elasticities. To that end, I first set out a model in which local

governments choose regulatory policy by trading off air quality against the economic bene-

fits associated with polluting firms. Because pollution can travel and firms have an incentive

to locate where regulation is least burdensome, a jurisdiction’s regulation can affect not only

its own firms and local pollution but also firms and pollution elsewhere. Such spillovers

motivate strategic regulation-setting whereby jurisdictions weaken their regulations to un-

dercut their competitors in the hope of attracting and retaining firms; jurisdictions will also

exploit favorable pollution export by weakening their regulations while those bearing the

transboundary pollution tighten their regulations to counteract its effect.

The model generates three estimating equations containing the elasticities of interest:

the first two describe how the count of firms and air pollution (respectively) are determined

in a given jurisdiction, both by regulation there and from elsewhere through spillovers; the

third describes how a jurisdiction will set its regulations, particularly in response to regu-

lations chosen by other jurisdictions.4 This setup, essentially involving a model of Cournot

competition, highights two identification issues, both of which arise because regulation is en-

dogenous. First, identifying the effect of regulation on firms and pollution is difficult because

regulation is in part determined by firms and by pollution levels. Second, the simultaneity of

interjurisdictional regulation-setting makes identifying how a jurisdiction’s regulatory choice

is affected by others’ policies difficult. But an appealing feature of the linear reaction func-

tion framework is that identification issues are well-understood: identification is based on

the use of instruments generated by plausible exclusion restrictions (see Blume et al. (2010)

for a recent survey).

Two conditions generate the exclusion restrictions necessary to identify the relevant elas-

ticities. The first condition is that the regulated air pollutant is sufficiently short-lived, as it

is for total suspended particulates (the focus of this paper), so that transboundary pollution

is restricted by distance. This implies that pollution spillovers are regional: pollution emit-

ted in one jurisdiction increases pollution in nearby downwind jurisdictions, but not in every

jurisdiction.5 Further, this condition implies that local exogenous factors affecting pollu-

4This regulation reaction function is adapted from the standard linear framework for analyzing strategicpolicy interactions among local jurisdictions (see, for example, Bui (1998)). For a survey of empirical studiesthat examine interjurisdictional strategic policymaking, see Brueckner (2003).

5Several studies have used wind direction, which imposes restrictions on air pollutant transport, to identify

2

tion dispersion, such as wind speed that transports air pollutants elsewhere, can be used as

exogenous instruments for the regulation set by the jurisdiction bearing the transboundary

pollution.6 The second condition is that, taking policy as given, firms locate independently

of local meteorological factors, such as the wind speed at 2 kilometers above the surface,

which can export emissions.7 Because jurisdictions have dual concerns over local pollution

and attracting firms, exogenous factors affecting only pollution can be used as instruments

for endogenous regulation to identify the elasticity of firm location decisions with respect to

environmental regulation.

To illustrate the intuition behind the identification approach, consider a simple three-

jurisdiction example and the problem of trying to identify the effect of regulation by an

upwind jurisdiction (call it ‘jurisdicition 2’) on the policy choice of a downwind jurisdic-

tion (‘jurisdiction 1’). One cannot use the exogenous pollutant dispersants, such as local

weather, in jurisdiction 2 as instruments, since those likely affect jurisdiction 1’s regulation

choice through their effect on pollution there. Yet suppose jurisdiction 2 experiences trans-

boundary pollution from a third jurisdiction (‘jurisdiction 3’) that is sufficiently distant from

jurisdiction 1 so that it has no direct effect on pollution there (via the first condition above).

Then the exogenous variation in pollutant dispersants in jurisdiction 3, such as wind velocity

at high altitudes, will serve to exogenously shift the endogenous regulation in jurisdiction 2

because of the relationship induced through transboundary pollution. At the same time, the

exclusion restrictions ensure that exogenous variation in 3 will neither affect jurisdiction 1’s

pollution, because transboundary pollution cannot travel that far nor, therefore, its regula-

tion choice. Thus, jurisdiction 3’s local weather provides a suitable instrument for the effect

of jurisdiction 2’s regulation choice on jurisdiction 1’s regulation. A similar logic applies to

the other estimating equations.

To estimate the model, I focus on a unique institutional setting, namely the decen-

tralization of U.S. air pollution regulations from the federal to state governments during

1971–1990. This context is appealing for several reasons. First, by observing both federal

the effect of air quality on outcomes of interest. See, for example, Bayer et al. (2009) for the effect of airquality on housing values and Schlenker and Walker (2015) for the effect on health. In contrast to thoseinnovative studies, this paper takes pollution – both locally and upwind – as endogenous, with the modelsuggesting the use of exogenous dispersants affecting pollution as valid instruments.

6This identifying condition is analogous to conditions used to identify peer effects, which strategicregulation-setting can be viewed as a variant of, exploiting exclusion restrictions based on overlapping peergroups (see Bramoulle et al. (2009) and De Giorgi et al. (2010)), where peer groups in this case are determinedby transboundary pollution.

7A similar argument is made by Broner et al. (2013) to estimate the effect of environmental regulationon country-level shares of U.S. imports.

3

and state regulations within a given state, I can compare outcomes before and after the

transfer of regulatory authority to test whether decentralized regulation generates spillovers

that the federal government does not. Second, interstate pollution and competition for firms

are recurring sources of concern for U.S. policy-makers, as noted above, making the U.S.

an important venue for quantifying the effects of these spillovers. Third, the regulations

pertain to emissions of total suspended particulates (TSP), a pollutant that satisfies the

requirements for identification: TSP is short-lived and heavy in mass, meaning that once

emitted, it can only affect TSP concentrations in nearby downwind states.

Under the Clean Air Act of 1970, the Environmental Protection Agency (EPA) promul-

gated several nationally-uniform, industry-specific emission standards for new sources of air

pollution. These New Source Performance Standards were designed to “preclude efforts on

the part of States to compete with each other in trying to attract new plants and facilities

without adequate control” (U.S.H.R. (1970)) by imposing a maximum level of emissions

for new plants across the country. Although the EPA writes and enforces the standards,

any willing state can voluntarily assume enforcement authority within its borders from the

EPA. Since the standards remain statutorily unchanged, the only impact a state can have

relates to how it enforces the standards.8 A transfer of authority should result in a change

in enforcement activity if the two levels of government have different incentives; any differ-

ences in enforcement between the EPA and states should be reflected – following transfers

of authority – in changes in air pollution, the count of regulated firms, or both.

I use annual cumulative transfers of regulatory authority over the standards, which I

collected from the Federal Register, as a proxy for the state’s regulation over time. This

measure reflects how much, in a given year, a state exercises policy control over the standards,

ranging from no control, where all standards in the state are enforced by the EPA, to full

state control. With these data, I combine information on the count of potentially regulated

establishments from County Business Patterns and ambient air concentrations TSP from

the EPA’s Quick Look Report. For information regarding the direction of prevailing wind

currents, which determines the movement of transboundary pollution, I use data from the

National Oceanographic and Atmospheric Administration’s radiosonde database, from which

I also obtain meteorological data that exogenously shift local TSP concentrations.

Estimating the reaction function describing a state’s decentralization decision, I find

that a state’s choice is influenced in part by the decentralization choices of states that

8As Martineau and Stagg (2004) note, the “flexibility [to regulate plants] is in the enforcement of thestandards.”

4

generate spillovers affecting it.9 Specifically, I find evidence of ‘strategic complementarity’

in regulation-setting that leads to the race to the bottom: states competing for firms with

a given state have a significant, positive effect on that state’s decentralization choice. In

contrast, a state that is upwind, so potentially generating transboundary pollution, has a

significant negative effect on the downwind state’s decentralization choice, consistent with

the notion that pollution spillovers cause regulations across jurisdictions to be ‘strategic

substitutes.’ Although the evidence supports the prediction by Ogawa and Wildasin (2009)

that these spillovers have offsetting effects on policy, the fact that not all states incur both

types of spillover implies that policy distortions remain. As a result, decentralized regulation

is likely inefficient for at least the states that do not incur both types of spillover.

While these estimates provide evidence that endogenous decentralization is chosen strate-

gically, they provide no evidence as to whether the distortions from strategic policymaking

arise because of spillover effects influencing firms and air pollution. However, from estimat-

ing the firm count equation, I find that a one standard deviation increase in a state’s control

of the standards increases the count of regulated firms located there by 3.3% on average,

while a one standard deviation increase in decentralization to a competing state decreases

the count of firms in the state in question by 2% on average. This evidence complements

that in Holmes (1998) and Duranton et al. (2011), who find that when faced with different

policies, firms choose to locate in the jurisdictions where the policies impose lower costs.10

The evidence that firms are locating in states that hold regulatory authority suggests that

state enforcement is less costly to polluters than EPA enforcement, which is consistent with

the prediction that states are less stringent than the central authority.

With respect to the effect on air pollution, I find that decentralization to a state has

little effect on TSP there. Decentralization to an upwind state has, on average, a negligible

effect on TSP concentrations in the downwind state, but this effect becomes positive the

smaller is the distance between the two states: decentralization to an upwind state within

200 kilometers of a given state’s centroid increases TSP in the latter by 2.3% on average.

The evidence of an increase in transboundary pollution following regulatory decentralization

echoes the findings in Sigman (2002) that international waterways are more heavily polluted

because countries are less likely to bear the full cost of their emissions, and the findings in

9Fredriksson and Millimet (2002) test for strategic interactions in state-level measures of abatement costsfrom environmental regulation, and find a positive association among neighboring states, although they arenot able to distinguish between competition and pollution spillovers.

10A long literature, starting with Bartik (1991), has found that interjurisdictional policy differences haveno effect on the location of businesses, though much of that literature assumes policy to be exogenous.

5

Sigman (2005) that, in a legislative setting similar to the one in this paper, decentralization

in the U.S. increases interstate water pollution.

Overall, I find that states holding regulatory control increase both the number of firms

locating there and transboundary pollution emanating from them, suggesting that states are

less stringent enforcers of the emission standards than the EPA. Along with the reaction

function estimates, this evidence points to the conclusion that states undertake voluntary

decentralization in order to attract firms even though it comes at the expense of other states’

firms and pollution levels, likely making decentralized environmental regulation inefficient

relative to centralized regulation.

2 Model of interjurisdictional environmental regula-

tion

The goal of the model presented in this section is to provide an estimation framework for

evaluating the effects of endogenous regulatory decentralization. To that end, there are two

steps toward achieving this goal. The first is that the model describe how local governments

choose regulatory decentralization and how outcomes, in the form of regulated industry and

air pollution, are affected not only by decentralization to a given government but also through

spillovers arising from endogenous decentralization by other governments. The second step

is to specify under what conditions the parameters of the model are identified, without an

instrumental variable or natural experiment that exogenously varies decentralization that

does not directly affect industry or pollution, since such instruments or experiments are

elusive in practice. The following sections describe the model and identification arguments

in detail.

2.1 The model

The model is adapted from classic models of interjurisdictional environmental policy-setting,

notably Oates and Schwab (1988) and, more recently, Ogawa and Wildasin (2009). Economic

actors are jurisdictions, of which there are a finite number, J . Each jurisdiction, j, chooses

a regulatory action described by the continuous variable dj ∈ D, where D is an interval.11

In the context of regulatory decentralization, dj represents the degree of regulatory control

11Though the model can be applied to continuous regulation of any kind, in the application below D =[0, 1].

6

held by a local government.12

Each jurisdiction sets its regulatory policy partly out of concern for air quality and also

in the interest of attracting or retaining regulated firms. Air quality in jurisdiction j is

measured negatively by pj, defined as the logarithm of ambient air concentrations of the

pollutant, while nj denotes the logarithm of the count of regulated, polluting firms in j.

The policy choice dj affects these two outcomes. Also potentially affecting these outcomes

because of transboundary pollution and the mobility of firms are the regulatory policy choices

of other jurisdictions, summarized by the vector d−j = (d1, . . . , dj−1, dj+1, . . . , dJ) of length

J − 1. Although d−j is written as a (J − 1)-tuple, I will think of it as a column vector. Both

of the outcomes nj and pj affect the jurisdiction’s payoff, Vj, which can be abstractly written

as

Vj = V (nj(dj,d−j), pj(dj,d−j), dj, εj).

Pollution decreases the payoff and the number of firms generate economic benefits that

increase the payoff, all else being equal. The variable εj is an unobservable determinant

of the payoff, such as the administrative cost of enforcing the policy dj. The jurisdiction

chooses dj to maximize Vj, given the choices of other jurisdictions, leads to a first-order

condition that is the jurisdiction’s reaction function.

The ambient air pollutant concentration, pj, is determined by emissions produced in that

jurisdiction and emissions produced elsewhere that can settle in j. A necessary condition for

such air pollution spillovers to occur is that the wind travel from the jurisdictions creating

the emissions to where the pollution is ultimately borne. Thus, the jurisdictions generating

emissions that raise pollutant concentrations in jurisdiction j are upwind of j; any jurisdiction

that is not upwind of j cannot directly affect pj. To incorporate this assumption, suppose

that 1pji = 1 if i is upwind of j, and is equal to 0 otherwise. Then the row vector 1pj of length

J−1 indexes which jurisdictions are upwind of j. For certain pollutants that can be emitted

anywhere and affect global pollution uniformly, like carbon dioxide, 1pji = 1 for all i and j;

for pollutants with shorter lifetimes, like total suspended particulates, pollution spillovers

across jurisdictions is regional instead of global.

Although aggregate (and disaggregated) emissions are observable in modern data sets,

emissions are not observed for the time period and pollutant considered in the empirical

section in this paper. I assume that emissions in j are produced as a linear function of

12As described in Section 3, the decentralization choice studied in this paper is made by local governmentsand not, wholly or in part, by the central authority. If instead decentralization was the result of a jointdecision, a model of bargaining would be more appropriate.

7

nj and the policy choice dj. Since for most pollutants ambient air concentrations can be

well-approximated by a linear function of emissions,13 the pollution concentration in j is

then

pj = β0 + β1dj + β2nj + β31pjd−j + β41

pjn−j + β5wj + β61

cjw−j + ξj. (1)

Because there are spillovers across jurisdictions, it may be that the errors in one jurisdiction’s

outcomes are correlated with those in another’s. To formalize this notion, suppose that ξj,

the error term in the pollution, is given by ξj = ξj + βξ1pj ξ−j, where each ξi is independently

distributed and βξ is a constant. Thus, ξj is affected by the unobserved determinants of pol-

lution not only in j but also from unobserved determinants of pollution from any jurisdiction

upwind of j.

The parameters of interest in equation (1) include the direct effects of the regulatory pol-

icy choice, β1, and the policy choices of upwind jurisdictions, described by β3. For example,

if larger values of the policy describe less stringent regulation, then these parameters should

be positive. The effect of regulated, polluting firms is described by β2 for j’s firms and β4

for firms in upwind jurisdictions; since the presence of more polluting firms mechanically

increases emissions, these parameters are positive. How meteorological conditions affect pol-

lution are described by β5 and β6, the effects of which depend on the variable. For example,

if wj represents wind speed in j, then because wind may disperse emissions from j, β5 should

be negative while β6 may be positive.

Equation (1) includes the number of firms, which is an endogenous variable. The count

of firms in j are affected by the policy choice dj as well as the policy choices of other

jurisdictions. Not all jurisdictions’ regulations will affect nj; only those jurisdictions whose

policies have the ability to lure firms away from j can directly affect nj. To incorporate

this assumption, define 1cji to be equal to 1 if jurisdiction i competes for j’s firms, and 0

otherwise. Then the logarithm of the count of firms in j is given by the function

nj = γ0 + γ1dj + γ21cjd−j + νj, (2)

where 1cj is the row vector of length J − 1 describing the competitors to j, and νj represents

unobserved factors affecting nj.14 Suppose also that νj, the error term in equation (2),

13The assumption that pollution concentrations is a linear function of emissions is because this relationshipis approximately linear for most common air pollutants. A notable exception is ground-level ozone; seeSeinfeld and Pandis (2006) for more on modeling ambient air pollutant concentrations.

14One could also include observable non-policy determinants of nj , such as population in j. However,such factors may directly affect pollution so do not contribute to the identification arguments made below,though such factors are used in both equations in the estimation section.

8

is given by νj = νj + γν1cj ν−j, where the νi are independently distributed and γν is a

constant. Therefore, the correlation between non-competing jurisdictions’ error terms in the

firm equation is zero, while for competitors the errors are correlated.

The parameters of interest in equation (2) are γ1 and γ2, which should be opposite in

sign if firms respond to interjurisdictional policy differences. For example, if increasing

values of the policy choice weaken environmental standards, then an increase in dj makes j a

more attractive location for cost-minimizing firms. As a result, one would expect that γ1 is

positive, while other jurisdictions’ increasing policy values drive firms away from j, implying

γ2 is negative.

The optimal choice of dj maximizes the payoff Vj, which is a function of the outcomes

nj and pj. To put the reaction function into the form used in the remainder of the paper,

suppose that nj, pj, and εj are additively separable in the payoff function and that Vj is

quadratic in dj.15 Then the reaction function, characteristic of strategic policy interaction

models as well as peer effects models, is given by16

dj = π0 + π11cji(1− 1pji)d−j + π2(1− 1cji)1

pjid−j (3)

+π31cji1

pjid−j + π4wj + π51

pjiwi + ηj.

Each jurisdiction j sets its policy according to equation (3). Because of the linearity of the

reaction functions, if an equilibrium exists, it is unique.

Equation (3) specifies how dj is determined in part by local conditions, through π4wj

and ηj, the error term, but also by policies and characteristics of other jurisdictions. All

of the parameters are functions of the outcome parameters from equations (1) and (2) and

any preference parameters from the payoff function. The strategic interactions in this model

are described by the parameters π1, π2, and π3. The parameter π1 describes the effect

that competing jurisdictions’ policies have on dj: if firms are mobile, then j will have to

mimic competitors’ policies to retain firms and thus π1 is positive, implying policies are

strategic complements. The parameter π2 describes the free-riding effect from transboundary

pollution: whatever effect an upwind jurisdictions’ policy has on pollution in j will induce

j to do the opposite of the upwind jurisdiction, implying π2 < 0 and that interjurisdictional

15For example, Vj could take the specification Vj = dj(bnj − pj − εj), where b > 0 is a constant.16Motivated and generated by the linearity of emissions in the pollutant concentration function, this

specification is the same as linear-in-sum peer effects models (see Ballester et al. (2006) and Calvo-Armengolet al. (2006) for examples). The alternative specification is the linear-in-means peer effects model, which isoften used in the context of education; see Cooley Fruehwirth (2013) for a recent example.

9

policies are strategic substitutes.17 The parameter π3 describes the effect on dj of policies by

jurisdictions that are both competitors and upwind. This parameter combines the effects of

competition and free-riding, which implies the prediction that π1 ≥ π2 ≥ π3. This transitivity

is important since, as Ogawa and Wildasin (2009) show, the two strategic incentives could

offset one another, potentially yielding π3 = 0, in which case decentralization is efficient.

2.2 Identification

Necessary for determining how the parameters in this model are identified is to specify

how the pollution determinants, wj, are related to the error terms εi, νi, and ξi for all i

and j. I assume that wj is weather affecting air pollutant dispersal and transport in j,

such as wind velocity or the difference between surface temperature and lower-tropospheric

temperature which measures how easy emissions can be dispersed from a location, and that

they are independent of unobserved determinants of firms, pollution, or the policy for all

jurisdictions i. This means that (i) firm location decisions are not determined by the wind

velocity (other than via regulation), (ii) unobserved determinants of local pollution, such

as human-made contributions to emissions, are independent of the wind velocity, and (iii)

unobserved determinants of the policy, such as budgetary conditions, are also independent

of wind velocity.

The model in this paper can be characterized as a peer effects model with linear reaction

functions. As Manski (1993) shows, this model is not identified without restrictions on

which jurisdictions affect other jurisdictions. Moffitt (2001) notes that exclusion restrictions

with exogenous variables can lead to an instrumental variables approach, but coming up

with plausible restrictions and plausibly exogenous variables can be difficult. In this model,

the critical exclusion restrictions are determined by pollutant lifetime and which direction

wind currents travel. The exogenous variables are meteorological factors that contribute to

or disperse local air pollution. These identifying assumption rely on the assumption that

jurisdictions choosing air pollution regulation do so in part because of concerns about air

quality. This approach is not only useful for identifying the reaction function but also in

identifying the effect of endogenous regulatory policies on firms and pollution. I discuss the

identification of each of the reaction function, firm count equation, and pollution equation

in turn.

17This strategic substitution between dj and its upwind neighbors’ policies is a classic feature of voluntarycontribution public goods games (such as in Bergstrom et al. (1986)).

10

2.2.1 Identification of the reaction function

The reaction function in (3) possesses classic endogeneity problems that make identification

challenging. Each of the three policy variables on the right-hand side – the competitor

peers, the upwind peers, and the upwind-competitor peers – is correlated with the error

term ηj because of simultaneity and because νj and ξj are functions of other jurisdictions’

error terms for the same reason that the other jurisdictions’ policy variables are part of the

reaction function.

Under certain conditions, the reaction function is identified. To see how, I consider the

two cases in which only one of competition for firms or transboundary spillovers arise, and

then the third case that includes both. Suppose first that there are is no transboundary

pollution. If competitors have any effect on j, it will be to induce j to mimic what they do

in order to retain firms. This effect implies that π1 > 0. The simultaneity of dj and di, for

competitor i, may lead to a positive bias of the parameter π1, but the omitted variable bias

has the opposite effect (if γν is negative), resulting in a bias of ambiguous sign. Notably

though, wj has a direct effect on dj but, since there is no transboundary pollution, has no

direct effect on any other jurisdiction’s policy choice. As a result, one can use the set of

weather variables of a competitor, of a competitor’s competitor that is not the competitor

of i, and so on, as exogenous shifters of the variable di for competing jurisdictions i. This

approach is valid even if competition is global, in which case one can use wi only as an

instrument for di.

Suppose now that there is no competition for firms, so that π1 and π3 are zero, but

there is transboundary pollution. In this case, dj is affected by jurisdictions that generate

transboundary pollution, and these jurisdictions’ policy choices induce j to free ride off

of upwind jurisdictions’ stringent regulations (or the upwind jurisdictions free ride off of

pollution export to j). This implies that π2 < 0. The simultaneity bias generates a bias

toward zero, as does the omitted variable bias (if βξ > 0). Unlike the case of competition,

the weather variable of an upwind jurisdiction i, wi, cannot be used as an instrument for

di, since wi affects emissions transport into j and therefore indirectly affects dj through its

direct effect on pj. As a result, for a given jurisdiction i upwind of j, identification requires

that there exist at least one k for which 1pjk = 0 and 1pik = 1; in words, the policy choice

of an upwind jurisdiction i is exogenously shifted by the weather variables in a jurisdiction

k that is upwind of i but not j. This is the same type of exclusion restriction that arises

in peer effects models with overlapping peer groups, which require multiple peer groups to

11

identify the peer effects (see Bramoulle et al. (2009) and De Giorgi et al. (2010)).18 This

rules out the identification of parameters when the pollutant is long-lived, like carbon dioxide

(CO2), which has an atmospheric lifetime of more than 100 years and is the main driver of

anthropogenic climate change. Emissions of CO2 anywhere in the world ultimately mix

uniformly across the world. In the context of the model, this would imply that 1pji = 1 for all

i, in which case there are no exclusion restrictions to exploit. Put another way, to identify

the parameters requires that the pollutant be a local public bad instead of a pure public

bad.

The general case is if transboundary pollution exists and firms are mobile, so that com-

peting jurisdictions may also impose pollution spillovers. In this case, identification of the

parameters in (3) is based on a similar identification strategy that requires that the pollutant

be a local public bad and that jurisdictions that are upwind of a given jurisdiction must also

have jurisdictions upwind of them.

2.2.2 Identification of the firm count function

There are two sources of endogeneity bias in the firms equation (2). The first is because

the error term, νj, partly determines the error term in the reaction function (3), implying

dj is correlated with the error term in the firm count function. The second is because νj is

correlated with vi for every competitor i, implying that di is also correlated with the error

term νj.

How the two correlations between errors and endogenous policies bias estimates depends

on what dj means and why jurisdiction choose certain values. For example, if increasing dj

causes pollution to increase because dj implies weaker regulation in j, then positive shocks

that increase firms in j will mechanically increase pollution in j, providing less of an incentive

to jurisdiction j to increase dj. As a result, the effect of the endogenous variable dj on firms

in j will suffer from negative bias toward zero.

Similarly, if jurisdiction i weakens its regulation, thereby increasing dj, the less costly

regulation will attract firms, decreasing firms in j. To counteract its competitors’ actions,

jurisdiction j will increase its own policy to retain its firms. Because the choice of i and

the response by j generate no interjurisdictional policy differences, firms will not have any

18One could also use the number of upwind jurisdictions, summarized by 1pj , as an instrumental variable.

This variable is the centrality measure used in Ballester et al. (2006) as an instrumental variable for endoge-nous peer effects. Because, given the data used in the empirical section, the number of upwind jurisdictionsdoes not vary much over time for a given jurisdiction, it precludes its use in the presence of jurisdiction fixedeffects, and so is not considered as a viable instrumental variable here.

12

incentive to move. In equilibrium, policies will have changed but the number of firms in j

will remain unchanged: firms will seem unresponsive, even though the causal effects of the

policies are non-zero.

This identification problem can be resolved because of the exclusion of the variables

wi for all i, including j, from the firms count equation (2). Under the assumption that

weather variables affecting the presence and dispersal of pollution have no impact on firm

location decisions other than through their effect on regulation, these variables constitute

valid instrumental variables. Furthermore, this equation is identified even if competition

is global. The only limitation is if pollution spillovers are global, in which case dj is a

function of every wi for all i. If the effect of wj on dj is the same in absolute value as its

effect on di for any competitor i, then there is no independent variation among the weather

variables to enable identification of the parameters. This would invalidate the use of the wi

as instrumental variables for policies affecting emissions of pollutants that are pure public

bads, such as climate change policies restricting emissions of carbon dioxide.

2.2.3 Identification of the pollution function

Like the firm count equation, (2), there are two sources of endogeneity bias in the pollution

equation (1). The first is because the error term, ξj, partly determines the error term in the

reaction function. The second is because ξj is correlated with ξi for every upwind jurisdiction

i. The sign of the correlation depends on βξ, which is likely positive if unobserved factors

increasing pollution in upwind i also increase emissions that are transported to jurisdiction

j.

If increasing dj causes pollution to increase (decrease), then positive unobserved shocks

that increase pollution in j make any increased (decreased) policy values more costly, imply-

ing a negative (positive) correlation between the error term and the policy variable, biasing

the estimate toward zero. Similarly, if positive, unobserved shocks to upwind pollution in-

crease pollution in j, and such shocks induce lower (higher) values of the policy, then the

bias on the upwind policy effect will be negative (positive).

The pollution concentration equation (1) is not identified because the rank condition –

that the effects of the excluded exogenous variables on the endogenous variables are linearly

independent – is not satisfied. The reason for this is because the model is linear throughout.

To see why the condition is not satisfied, note that an excluded exogenous variable is wi for

competitor i that is not upwind of j. The effect of wi, through di, on dj arises through the

competition effect on nj. Thus, the effect of wi, the excluded variable, on dj and nj is the same

13

up to a multiplicative constant. Any other excluded variables arising from non-competition

channels are through transboundary pollution, yielding a similar linear dependence. The

only resolution to the identification problem is an exogenous variable, say xj, that affects

the policy choice without affecting firms or pollution. Given how intertwined are pollution

and economic factors, finding such an exogenous shifter is a difficult task.

3 Data

This study uses data from multiple sources. Forming the centerpiece of these are episodes

of decentralization of enforcement authority over federal standards for the emissions of total

suspended particulates (TSP) by certain industries. With these I combine information on

firm counts and TSP concentrations, weather patterns that affect the airborne transport of

TSP, and other covariates. Summary statistics for all variables are listed in Table 2.

3.1 Decentralization of air pollution regulations

Industry-specific emission standards on new sources of TSP and other ‘criteria’ pollutants

were, along with the National Ambient Air Quality Standards, made a cornerstone of the

Clean Air Act of 1970 (CAA70). These ‘New Source Performance Standards’ (NSPSs)

were mandated by CAA70, promulgated gradually after its enactment and remain in effect

today.19 The goal of the program was to “level the playing field for states competing for

new industrial growth” (Martineau and Stagg (2004)) by establishing nationally-uniform

regulations for new industrial sources of emissions.

Although they are federal rules and enforced by default by the Environmental Protection

Agency (EPA), NSPSs could be enforced by any interested state or local government that

sought a delegation of enforcement authority for any combination of NSPSs at any time

from the EPA. In this paper, a transfer of enforcement authority constitues an episode of

decentralization.

To obtain information about an NSPS standard, I collected from the Federal Register the

following data: (i) when each standard was proposed and came into effect, (ii) the pollutants

that the standard regulates, and (iii) the industry affected by the standard. Each standard

regulates the emissions of one industry and affects the production of particular pollutants.

19Although many were revised following the passage of the Clean Air Act Amendments of 1990 (CAA90).The Clean Air Act Amendments of 1990 made major changes to federal air pollution regulations, so I restrictmy sample to the period after the passage of CAA70 and before CAA90.

14

The industries that were regulated first were the most prolific polluters nationally (see Reitze

(2001)). Because of constraints imposed by the pollution data, discussed below, I restrict the

analysis to TSP standards. Table 1 reports each standard used in these data, the affected

industry, and the year of promulgation. As the table shows, standards are promulgated in

nearly every year from 1971 to 1990.

Table 1: New Source Performance Standards by industry, pollutant, and promulgation year

Standard Industry Year

D Fossil Fuel-Fired Steam Generators 1971E Incinerators 1971F Portland Cement Plants 1971I Asphalt Concrete Plants 1974J Petroleum Refineries 1974L Secondary Lead Smelters 1974M Secondary Brass and Bronze Ingot Production Plants 1974N Iron and Steel Plants 1974O Sewage Treatment Plants 1974AA Steel Plants: Electric Arc Furnaces 1975P Primary Copper Smelters 1976Q Primary Zinc Smelters 1976R Primary Lead Smelters 1976Y Coal Preparation Plants 1976Z Ferroalloy Production Facilities 1976BB Kraft Pulp Mills 1978DD Grain Elevators 1978HH Lime Manufacturing Plants 1978Da Electric Utility Steam Generating Units 1979

Constructed After May 18, 1978CC Glass Manufacturing Plants 1980PP Ammonium Sulfate Manufacture 1980UU Asphalt Processing and Asphalt Roofing Manufacture 1982AAa Steel Plants: Electric Arc Furnaces 1984LL Metallic Mineral Processing Plants 1984OOO Nonmetallic Mineral Processing Plants 1985PPP Wool Fiberglass Insulation Manufacturing Plants 1985Db Industrial-Commercial-Institutional Steam 1986

Generating UnitsNa Basic Oxygen Process Steelmaking Facilities 1986

Constructed After Jan. 20, 1983AAA New Residential Wood Heaters 1988

Notes: Under the legislation for NSPS, 40 CFR Part 60, each standard is abbreviated by a letter code,provided above. Year denotes the year that the standard was promulgated.

Any transfer of authority for NSPS enforcement must be published by the EPA in the

Federal Register, from which I collected: (i) what state or local government has made an

15

NSPS request; (ii) the standard(s) requested by that government; (iii) the EPA’s response,

and (iv) if a delegation of authority is made, when the transfer of authority becomes effec-

tive.20

Almost all of the states seek authority to some extent, the one exception being Rhode

Island. Requests for delegation of authority are typically made by states, though some

are made by counties and municipalities. In this study, I include only contiguous states,

except California, and include the District of Columbia.21 Requests for authority are rarely

rejected, and authority is rarely revoked once delegated.22 There are no rejections of a state’s

request or revocations of a state’s delegated authority on the basis of the EPA’s concerns

over enforcement vigilance.

To describe the level of decentralization of NSPSs to states, I calculate what proportion

of all standards available for decentralization by a given year t had been decentralized to a

given state j. This can be achieved as follows: if the authority over standard k has been

decentralized to state j by year t, then djkt = 1; otherwise djkt = 0. If Kt is the total number

of standards made available for decentralization by year t, then the fraction of standards

decentralized to state j at time t is given by

djt =1

Kt

Kt∑k=1

djkt. (4)

As an example, suppose four standards have been promulgated by 1975. If the state of New

York has been delegated the authority over two standards, then dNY,1975 = 0.5. This measure

reflects the degree to which authority for standards are in a state’s control: if the measure

equals 0, then authority rests soley with the EPA; a state with the measure equal to 1 has

had authority fully decentralized to it.23

The first row of Table 2 reports summary statistics of the decentralization measure (4).

In the entire sample, states on average have authority for nearly 60% of standards. Figure 1

20An example is provided in Figure A.1 of Appendix A. I verified the existence of delegations with regionalEPA offices and/or through Freedom of Information Act requests.

21I exclude California because of its different form of decision-making: the state of California makes nodelegation requests and enforces no standards; instead, counties in California undertake such actions.

22From a reading of the Federal Register, it seems that the main reason for rejection is a lack of legalauthority to enforce a standard – typically, this means failure to incorporate the federal rule into the statecode.

23The metric (4) can be altered in any number of ways by incorporating a different weighting scheme, suchas by using industry-specific emissions factors. Since the above metric weighs all standards uniformly, it isrelatively conservative in the sense that it is likely less powerful in describing the effect of decentralizationon outcomes.

16

plots the metric for each state within two EPA regional offices.24 As the figures depict, there

is significant cross-sectional and temporal variation, although decentralization is on average

trending upwards. By 1990, states held about 80% of the standards in their control.0

.2.4

.6.8

1D

ecen

tral

izat

ion

mea

sure

1970 1975 1980 1985 1990Year

DEDCMDPAVAWV

(a) Region 3

0.2

.4.6

.81

Dec

entr

aliz

atio

n m

easu

re

1970 1975 1980 1985 1990Year

ARLANMOKTX

(b) Region 6

Figure 1: Decentralization measure across EPA Regions

3.2 Count of regulated establishments

The EPA does not maintain records of the number of polluting facilities or establishments for

the period 1971–1990. However, given that each NSPS regulates emissions for a particular

industry, I am able to determine the 4-digit Standard Industrial Classification (SIC) code

from each NSPS’s Federal Register documentation.25 The regulated industries are mostly in

manufacturing, mining, or power generation. To evaluate industrial outcomes as a result of

NSPS decentralization, I make use of the County Business Patterns to obtain the total annual

count of potentially regulated establishments in each year at the state- and county-levels.26

3.3 TSP concentrations

Total suspended particulates consist of airborne particles in liquid or solid form. The at-

mospheric lifetime – the length of time pollutants remain in the atmosphere until they are

24Figures depicting the metric for the remaining states are in Figure A.2 in the Appendix.25The SIC codes for each standard are reported in Table B.1 in Appendix B.26Because this measure represents potentially regulated establishments, it may mismeasure the actual

number of regulated establishment. To evaluate the robustness of my empirical results, I also use 2-digitSIC codes.

17

diffused or deposited back to the surface – of TSP ranges from 10 days to a few months, with

smaller-sized particles having longer lifetimes. The atmospheric lifetime of TSP implies such

particulates can travel up to hundreds of kilometers from where they were emitted. TSP can

thus be considered a local public bad that, in the U.S. context, can traverse state boundaries

but not the entire country. Based on these characteristics and the identification requirement

that the pollutant be a local public bad, TSP makes a suitable candidate to estimate the

model.

I use the EPA’s Quick Look Report database of ambient air concentrations of TSP

recorded from geographically-fixed monitoring stations spread across the country.27 For

each year, I calculate the annual geometric mean at the state- and county-level. Though

there is substantial variation across counties and states in the concentration of ambient air

TSP, TSP concentrations have decreased substantially during 1971–1990.

3.4 Weather patterns affecting airborne transport of TSP

The model calls for information on wind direction and meteorological factors affecting TSP

concentrations. To incorporate data on weather patterns, I use the Radiosonde Database

created by the Earth System Research Laboratory, a laboratory of the National Oceano-

graphic and Atmospheric Administration (NOAA).28 At pre-specified pressure levels, each

radiosonde records direction of prevailing wind (measured in degrees), among other vari-

ables. This study uses data collected at the earth’s surface and at 850 hectopascals (which is

typically around 1-2 kilometers above surface level), the latter being important for airborne

pollutant transport for relatively local pollutants like TSP. For information at non-recorded

locations (such as state or county centroids), I interpolated the annual means of the vari-

able in question by spatial regressions using geographic location and distance to nearest

radiosonde observations.29

The wind direction data allows me to specify whether state i is upwind of state j, which

27For the time period of this study, the EPA mains no firm-, county-, or state-level emissions inventory forTSP. The data in the Quick Look Report has been extensively used in the literature estimating the causesand effects of air pollution; for example, see Chay and Greenstone (2005).

28A radiosonde is a balloon released from ground level, which ascends and collects weather informationelectronically. Radiosondes are programmed to take recordings at pre-specified atmospheric pressure levelsthat are consistent across all radiosondes. The NOAA database contains radiosonde readings from 1946 tothe present day from locations all across North America.

29Specifically, I use a cubic polynomial in latitude and longitude and a cubic polynomial in neighboringradiosonde observations that are weighted by inverse-distance. This approach is conservative in that itunderestimates the mean and range of the observed data. See Luo et al. (2008) for a description andcomparison of the different interpolation techniques for meteorological data.

18

in the model corresponds to the indicator 1pji. Using prevailing wind direction at the lower

troposphere (850 hectopascals), I define i as upwind of j in a given year if the prevailing

wind at the geographic centroid in j is within 22.5 degrees of the direction of the geographic

centroid of state i. Since the pollutant can travel only so far, I reduce the number of upwind

states to within a certain distance of j; baseline specifications consider only adjacent states.

Figure 2 depicts wind currents in the from of arrows emanating from the geographic centroid

of each contiguous state (excluding California) for four of the years in the sample. For the

eastern part of the U.S., the wind consistently travels from west to east, whereas in other

parts the direction changes.

The model also calls for exogenous variables affecting the prevalence and dispersal of TSP.

In this paper, I use two weather variables: surface temperature and the difference between

surface temperature and the lower-tropospheric temperature (measured at 850 hectopascals),

which I will refer to as the temperature difference. Of all weather variables affecting TSP

concentrations, I use only these variables because they exhibit trends that are differential

across U.S. states during the time period of this study. This feature of these data permit me

to use state and time fixed effects in the estimation framework.30 Global and U.S. surface

and lower-tropospheric temperatures have been increasing annually since before the time

period in this paper (see, for example, IPCC (2013), Karl et al. (2006), and Thorne et al.

(2010)), and both of these temperature measures exhibit upward trends on average in the

data. However, since surface temperatures seem to be increasing at a greater rate in most

regions, this temperature difference is increasing on average, though in some regions it is

decreasing. Figure 3 plots the temperature and temperature difference variables for two

EPA regions.31

Surface temperature can increase TSP in water vapor through evaporation but has also

been found to increase emissions from a given source in colder temperatures (see Nam et al.

(2010) for an example), so the net effect is ambiguous. The difference in surface and lower-

tropospheric temperatures measures the extent to which emissions can be lifted upwards into

the atmosphere, where it is more likely they will be transported elsewhere.32 Since warm

30In addition to these two variables, one can use surface and lower-tropospheric wind speed, which hasbeen shown to be decreasing differentially across the U.S. for this time period due to a process called‘atmospheric stilling’ (see Pryor et al. (2009)). However, the trend in the wind speed variables, especiallythe lower-tropospheric, grows at such a small rate that much of the variation is absorbed by the fixed effects,an observation consistent with the findings in Pryor and Ledolter (2010). As a result, I omit using them inthe empirical analysis.

31Figures A.3 and A.4 depicts these variables for the remaining EPA regions in the Appendix.32This variable is somewhat similar to the ‘ventilation coefficient’ used by Broner et al. (2013) as an

instrumental variable for country-level environmental regulation in estimating its effect on country import

19

(a)

1975

(b)

1980

(c)

198

5(d

)19

90

Fig

ure

2:A

vera

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ind

curr

ents

for

sele

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ars.

Arr

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indic

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win

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20

68

1012

Sur

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tem

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(de

gree

s C

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1970 1975 1980 1985 1990Year

DEDCMDPAVAWV

(a) Region 3: surface temperature8

1012

1416

Sur

face

tem

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(de

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s C

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1970 1975 1980 1985 1990Year

ARLANMOKTX

(b) Region 6: surface temperature

23

45

6S

urfa

ce te

mp

- Lo

wer

trop

osph

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tem

p (d

egre

es C

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1970 1975 1980 1985 1990Year

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(c) Region 3: temperature difference

-4-2

02

46

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tem

p -

Low

er tr

opos

pher

e te

mp

(deg

rees

Cel

sius

)

1970 1975 1980 1985 1990Year

ARLANMOKTX

(d) Region 6: temperature difference

Figure 3: Surface temperature (◦C) and temperature difference (◦C) for different EPA Re-gions

21

air rises (and emissions with it), it is critical that this difference be positive; negative values

imply an air inversion where warm air acts as a ceiling on colder, near-surface air that traps

emissions.33

3.5 Other covariates

To control for factors affecting decentralization choices as well as outcomes, I utilize covariates

at the state- and county-levels. In particular, I include each state’s annual population, per

capita income, and annual gross domestic product, in 2015 dollars.34

Table 2: Summary statistics of state-level variables

Obs. Mean Std. Dev.

Decentralization 960 0.4978 0.4126Count of 4-digit SIC establishments 816 613 465TSP concentration (µg/m3) 947 50.16 11.66No. of adjacent states 960 2.26 1.07No. of upwind adjacent states 960 0.67 0.56Surface temperature (◦C) 960 7.65 3.55Temperature difference (◦C) 960 1.02 2.88Real GDP (billions in 2014 $) 960 149.57 153.12Population (100,000s) 960 42.12 38.99

4 Estimation and results

This section presents ordinary least squares (OLS) and instrumental variable (IV) of the

reaction function, equation (3), and the firm count equation, (2), as well as OLS estimates

of the pollution equation, (1). The model described in Section 2 prescribes the use of me-

teorological factors as instrumental variables, which I implement in a generalized method

shares into the U.S. Instead of the temperature difference, the ventilation coefficient is the product of windspeed and the height at which pollutants disperse.

33Air inversion episodes occur frequently and were the cause of several fatal smog events in the mid-dle of the twentieth century. Among those is the 1948 event in Donora, Pennsylvania; see ‘SmogDeaths In 1948 Led To Clean Air Laws’, All Things Considered from National Public Radio, availableat http://www.npr.org/templates/story/story.php?storyId=103359330.

34In robustness checks at county-level observations, I also use each county’s annual population and percapita income, in 2015 dollars, as well as an indicator variable describing whether a given county in a givenyear was in nonattainment status under the National Ambient Air Quality Standards for TSP.

22

of moments (GMM) framework. Each table of estimation results reports conventional stan-

dard errors, based on asymptotic theory. Given the sample of decentralization episodes and

outcomes in this paper constitutes the population, these standard errors are not necessarily

correct; the true standard errors lie between zero and the ones reported here.35

The empirical framework in this section categorizes, relative to the state in question,

only adjacent states as competitors and only upwind adjacent neighbors as generating trans-

boundary pollution.36 Section 5 explores the use of alternative definitions of competing and

polluting states, based on the distance to a given state, and how the estimates of the three

estimating equations change as a result.

4.1 First-stage estimates

The reduced-form version of equation (3) describes how the variable dj, representing regula-

tory decentralization, is determined by the following set of exogenous variables: j’s weather

variables, its non-upwind and upwind neighbors’ weather variables, the peers of those neigh-

bors, and so on. I limit the estimation to include the peers of j’s peers, since including these

jurisdictions is necessary for identification. In addition to these variables, the full specifica-

tion contains state fixed effects, year fixed effects, and state-level economic controls for the

state in question and all of its neighboring states.

Table 3 reports estimation results of the reduced-form decentralization equation. Each

column includes additional controls relative to the previous column. Column (1) includes

no controls other than the weather variables, column (2) adds state fixed effects, column (3)

adds year fixed effects, and the final column adds the economic controls. Because of space

constraints, only the variables for adjacent states are reported. The first two rows in column

(1) report estimates of the effect of a state’s own weather variables on its decentralization

choice. The results indicate that a 1◦C increase in temperatures decreases the decentraliza-

tion choice by 0.0097; put another way, a one-standard-deviation increase in temperatures

decreases the decentralization measure by about 8% of a standard deviation. The effect of

the temperature difference on decentralization is negligible.

Focusing on the first two rows, but moving across the columns, the coefficient estimates

35Recently, Abadie et al. (2014) have provided a framework for estimating finite population causal standarderrors, which would apply here. As they point out, the true standard errors are between zero and errorsbased on asymptotic theory.

36Given how large and capitalize-intensive these industries are, it is unlikely that they are footloose enoughto move across the country. As Hillberry and Hummels (2008) notes, most firms do not ship products morethan 200 miles from their location, implying that the markets in question may overlap boundaries of adjacentstates but do not extend much farther.

23

Table 3: First-stage estimates: the reduced-form decentralization equation

(1) (2) (3) (4)

Surface temperature -0.0097 -0.0051 -0.0259∗∗ -0.0250∗∗

(0.0095) (0.0103) (0.0113) (0.0109)Temperature difference 0.0000 0.0129 0.0254∗∗∗ 0.0231∗∗∗

(0.0196) (0.0138) (0.0093) (0.0085)Non-upwind surface temp. 0.0041∗ 0.0068∗∗∗ 0.0081∗ 0.0082∗∗

(0.0021) (0.0020) (0.0040) (0.0039)Non-upwind temperature diff. -0.0006 -0.0039 0.0002 0.0010

(0.0049) (0.0033) (0.0043) (0.0040)Upwind surface temp. 0.0047 0.0060 0.0096∗∗ 0.0053

(0.0050) (0.0048) (0.0046) (0.0058)Upwind temperature diff. 0.0045 -0.0004 -0.0038 -0.0017

(0.0152) (0.0127) (0.0076) (0.0075)Log(population) 0.7145∗

(0.3646)Log(GDP) -0.4375∗

(0.2461)Non-upwind log(population) -0.0217

(0.0159)Non-upwind log(GDP) 0.0249

(0.0171)Upwind log(population) 0.0242

(0.0312)Upwind log(GDP) -0.0310

(0.0371)

State FE N Y Y YYear FE N N Y YR2 0.0217 0.2372 0.7355 0.7427Observations 960 960 960 960

Notes: The dependant variable is the decentralization measure. Robust standard errors, adjusted forclustering at the state-level, in parentheses. ∗, ∗∗, and ∗∗∗ denote estimates different from zero at the10%, 5%, and 1% significance levels.

24

stay the same sign but change magnitude. The changes in magnitude are unsurprising:

weather variables exhibit strong interstate differences, as does decentralization; decentraliza-

tion is trending upwards over time, meanwhile these specific weather variables were selected

because they exhibit temporal trends. As a result, controlling for state-specific differences

and common time trends should change the coefficient estimates. Upon controlling for the

fixed effects, the estimates remain stable even after controlling for economic factors affecting

decentralization choices, such as state-level GDP and population. Column (4) reports esti-

mates from employing the full specification; focusing on the first two rows in that column,

the coefficient estimates reveal that a state’s weather variables have a significant effect on

its decentralization choice. In particular, the coefficient estimate for surface temperature,

−0.0250, implies that a one-standard-deviation increase in temperature causes a decrease on

average in the decentralization measure that corresponds to 21% of a standard deviation.

The coefficient estimate for temperature difference in column (4) is positive, as it is in ev-

ery column, equal to 0.0231, and implies that a one-standard-deviation in the temperature

difference increases the decentralization measure on average by 16% of a standard deviation.

These coefficient estimates are informative for what they imply about incentives to seek

regulatory decentralization. Consider the temperature difference variable, which unambigu-

ously decreases pollution by ventilating emissions upward into the troposphere, where they

can be transported elsewhere by wind currents. The coefficient estimates in Table 3 for

temperature difference are all positive. This evidence suggests that decreases in air pollu-

tion – caused by increases in the temperature difference – cause decentralization to increase.

Thus, decentralization by a state increases when TSP concentration levels there are lower.

If states use decentralization to more vigilantly enforce the standards relative to the EPA,

then the reduced-form effect of the temperature difference variable would be negative: a

weaker pollution export technology would require stricter control of pollution sources, so

lower temperature difference levels would cause states to seek decentralization. Instead,

these coefficient estimates are consistent with the notion that states may be seeking decen-

tralization in order to more flexibly (and/or weakly) enforce the standards relative to the

EPA, and increases in temperature difference levels decrease the marginal cost of doing so.

The effects of neighboring states’ weather variables are similarly illuminating. Focusing

on column (4), rows three and four include the weather variables for adjacent, but non-

upwind states. In the context of the model, these states are exclusively competitors with

the state in question. If these states do compete with one another, then the sign of each

variable should be similar to the sign of the same variable for the own-state: if the tem-

25

perature difference increases in a competitor’s state, this will induce that state to increase

its decentralization measure (for the reasons described above); doing so lures firms from the

state in question away, which induces the state in question to seek more decentralization

to counteract the competing state’s effect. As a result, states’ decentralization choices are

‘strategic complements’, and any exogenous factor affecting one state should have a similar,

but lower in magnitude, effect on a competing state. This prediction is supported by the

coefficient estimate for a non-upwind (competing) state’s temperature difference variable,

reported in the cell in row 5 of column (4): equal to 0.0053, the estimate implies that a

one-standard-deviation increase in the non-upwind state’s temperature difference increases

decentralization in the state in question by about 1% of a standard deviation on average.37

Since it potentially imposes pollution spillovers, the effect of an upwind state that is

also a competitor should differ from that of a state that is exclusively a competitor. In

particular, an increase in the temperature difference level in an upwind state may induce it

seek decentralization, compelling the downwind state (in question) to do the same so that

their policies are ‘strategic complements.’ However, if decentralization leads to an increase

in emissions, more decentralization by an upwind state will, through transboundary pollu-

tion, increase pollution in the downwind state, inhibiting the downwind state from pursuing

decentralization, making decentralization choices ‘strategic substitutes’ across states. As a

result, the offsetting ‘strategic substitutability’ due to pollution spillovers will potentially

offset the ‘strategic complementarity’ from competition, implying that the effect of weather

variables in an upwind state to be lower in value than the same variables in a non-upwind

state.38 The coefficient estimates for the upwind state’s temperature and temperature dif-

ference are consistent with this prediction: both coefficient estimates, 0.0053 and −0.0017,

are lower in value than the same coefficient estimates for the non-upwind state, 0.0082 and

0.0010, respectively. As a result, this reduced-form evidence suggests that competition and

pollution spillovers may affect states’ decentralization decisions, and foreshadows what re-

sults to expect from estimating the reaction function in its structural, as opposed to reduced,

form.

Altogether, these estimates show that the weather variables are economically significant

and meaningful determinants of a state’s decentralization choice, even after controlling for

37Not all coefficient estimates match in the same way though: the coefficient estimate for surface temper-ature for the non-upwind state differs in sign to the own-state’s temperature variable. This may be due tothe ambiguity in the sign this variable’s own-state effect on TSP.

38Additionally, the upwind temperature difference ventilates emissions from the upwind jurisdiction, mak-ing transport (to the downwind state) more likely, thereby directly causing more pollution.

26

state and year fixed effects and several relevant economic factors. This implies that decen-

tralization can, in part, be explained by weather variables in that state and in neighboring

states, which is critical for their use as instrumental variables in estimating the equations of

interest, considered in the following subsections.

4.2 Estimates of the reaction function

By incorporating the year index, t, I augment the reaction function, equation (3), to take

the following form:

djt = π0 + π11cj(1− 1pjt)d−jt + π21

cj1

pjitd−jt + wjtΠ5 (5)

+w−j1pjtΠ6 + ZjtΘ + λj + λt + ηjt,

where most the variables are the same as in equation (3).39 The vector wjt contains the set

of a state’s weather variables (temperature and temperature difference) and the matrix Zjt

includes information on state-level economic factors – the logarithm of real GDP, in billions

of 2014 dollars, and the logarithm of population, in hundred thousands – for the state in

question, as well as for the non-upwind and upwind adjacent states. The parameters λj and

λt are state and year fixed effects, respectively.

Table 4 reports estimates of equation (5). Panel (A) reports estimates from ordinary

least squares, while panel (B) reports instrumental variables (IV) estimates estimated using

GMM. Column (1) includes only the decentralization measures for adjacent states (upwind

or non-upwind), and each column adds progressively more controls. At the bottom of each

column in Panel A are reported the R2 values, while at the bottom of each column in

Panel B are the first-stage F statistic and the p-value for the J-statistic on overidentifying

restrictions.

Consider first panel (A), which reports estimates using ordinary least squares. The speci-

fication in column (1) includes no controls other than the variables of interest. The coefficient

estimates for both categories of adjacent states are positive, implying that decentralization

in a given state is positively affected by what neighboring states do. The coefficient estimate

for decentralization by a non-upwind state, 0.1592, implies that a one-standard-deviation

increase in a single non-upwind state’s decentralization increases the state’s decentralization

choice by about 15% of a standard deviation. Similarly, a one-standard-deviation increase

39Because competitors and upwind states are specified as adjacent states, there are no states that areupwind but not competitors.

27

in a single upwind state’s decentralization measure increases the state’s decentralization by

nearly 18% of a standard deviation. In column (5), the full specification, the coefficient es-

timate for non-upwind adjacent states is 0.0713 and indicates that a one-standard-deviation

increase in a single non-upwind state’s decentralization increases a state’s decentralization

choice by 7%. The effect of upwind state decentralization, described by the coefficient esti-

mate 0.0690 is slightly lower in magnitude than the effect for a non-upwind state.

The model predicts that the coefficient estimates from ordinary least squares will be

biased toward zero. This implies that the non-upwind decentralization measure, which is

predicted to be positive, is likely higher in value than the coefficient estimate reported in

column (5) of panel (A). Since this estimate forms a lower bound for the causal effect of (non-

upwind) competing states, this result provides suggestive evidence that states do engage in

behavior that is consistent with the theory of competition for mobile factors.

Panel (B) of Table 4 reports coefficient estimates from instrumental variables estimated

using GMM. The estimates are directly comparable to the OLS estimates in the top panel

from the same column. The estimates for both categories of adjacent states’ decentralization

are positive throughout. After controlling for state fixed effects in column (2), the coefficient

estimates for non-upwind decentralization are relatively stable. In the full specification,

column (5), the coefficient estimate for non-upwind decentralization, 0.2813, is larger in

magnitude than the OLS estimate in column (5), thus supporting the prediction of negative

bias in the OLS coefficient estimate. The estimate implies that a one-standard-deviation

increase in a single non-upwind, adjacent state increases the state in question’s decentraliza-

tion choice by nearly 30%. Put another way, if a non-upwind, adjacent state underwent a

change from no state control of the standards to full decentralization, the state in question

would increase its decentralization measure by 0.3; if more than a single non-upwind state

does so, the state would choose near-total decentralization. This evidence provides strong

evidence supporting the prediction of ‘strategic complementarity’ of interjurisdictional com-

petition: jurisdictions that compete for mobile firms must mimic one another’s policies in

order to avoid a competitive disadvantage in attracting and retaining firms.

Because upwind states are categorized as generating both competition and pollution

spillovers, the simultaneity of decentralization choices between a state and its upwind neigh-

bors generates a bias of ambiguous sign. The second row of Panel B presents causal estimates

of the effect of upwind states on a state’s decentralization choices. The IV coefficient esti-

mates change in magnitude for the first three columns but remain stable after controlling for

weather variables. Not controlling for these variables likely causes an omitted variables bias

28

Table 4: Estimates of the decentralization reaction function

(1) (2) (3) (4) (5)

Panel A: OLS estimates

Non-upwind decentralization 0.1502∗∗∗ 0.1874∗∗∗ 0.0697∗∗∗ 0.0724∗∗∗ 0.0713∗∗∗

(0.0160) (0.0118) (0.0219) (0.0220) (0.0228)Upwind decentralization 0.1796∗∗∗ 0.1900∗∗∗ 0.0668∗ 0.0477 0.0690

(0.0499) (0.0340) (0.0389) (0.0424) (0.0437)R2 0.4417 0.6881 0.7456 0.7477 0.7536

Panel B: IV estimates

Non-upwind decentralization 0.0706∗∗ 0.1829∗∗∗ 0.3353∗ 0.2117 0.2813∗∗∗

(0.0340) (0.0342) (0.1774) (0.1633) (0.0936)Upwind decentralization 0.0564 0.0807 0.4392 -0.0766 0.0306

(0.0994) (0.1571) (0.3059) (0.6356) (0.5995)F statistic 36.0903 2.8361 0.8875 0.3732 0.8251Overid. p-value 0.7937 0.6307 0.4362 0.7887 0.7421

State FE N Y Y Y YYear FE N N Y Y YWeather controls N N N Y YEconomic controls N N N N YObservations 960 960 960 960 960

Notes: The dependant variable is the decentralization measure. Weather controls include surfacetemperature and temperature difference, for the state and the upwind states. Economic controls includethe logarithm of state GDP and population, for the state, competitor states, and upwind states. IVestimates from GMM. Robust standard errors, adjusted for clustering at the state-level, in parentheses.∗, ∗∗, and ∗∗∗ denote estimates different from zero at the 10%, 5%, and 1% significance levels.

29

problem: the weather variables from the upwind state cause that state’s decentralization,

but are included in equation (5) because they directly affect pollution in the state in ques-

tion. In column (5), the IV estimate for the coefficient on upwind decentralization, 0.0306,

is lower in value than the OLS estimate, 0.0690; this observation suggests that the net bias

on the OLS estimate is positive. The coefficient estimate is telling in comparison to the

IV estimate for non-upwind decentralization. The latter estimate is from decentralization

to states categorized as competitors to the state, which generate policy distortions because

policies across states become ‘strategic complements,’ while upwind states are capable of

imposing pollution spillovers and being competitors, generating both ‘strategic complemen-

tarity’ and ‘substitutability.’ The difference between the estimate for the non-upwind state,

which is a competitor only, and the estimate for the competitor upwind state describes the

free-riding effect that arises from ‘strategic substitution’ of policies. The difference is equal

to −0.2507, which is entirely consistent with free-riding: if regulatory decentralization is

used to weaken environmental standards, then decentralization to an upwind state increases

local TSP in the local state, making decentralization more costly to that state and inducing

less decentralization.

The result that upwind-competitor states have a smaller absolute effect than competitor-

only (or upwind-only) states supports the prediction by Ogawa and Wildasin (2009) that

competition and pollution spillovers can potentially offset one another. As that study shows,

if the spillovers perfectly offset one another, then decentralized environmental regulation is

efficient. In the context of this study, decentralization is not efficient, since not all states

incur both spillover. As a result, policy distortions remain: while all states face ‘strategic

complementarity’ with competing states’ decentralization, some states lack the ‘strategic

substitution’ from pollution spillovers to offset the first spillover.

4.3 Estimates of the firm count function

By incorporating the year index, t, I augment the firm count function, equation (2), to take

the following form:

njt = γ0 + γ1djt + γ21cjtd−jt + ZjtΓ5 + ρj + ρt + νjt. (6)

Most of the variables are the same as in equation (2). The matrix Zjt includes the logarithm

of real GDP, in billions of 2014 dollars, and the logarithm of state population, in hundred

thousands, for a state and its competitor states. The parameters ρj and ρt are state and

30

year fixed effects.

Table 5 reports estimates of the effect of decentralization on the logarithm of the count

of 4-digit SIC establishments in a state.40 Panel (A) reports OLS coefficient estimates and

panel (B) reports IV coefficient estimates, estimated using GMM.

Table 5: The effect of decentralization on the count of state-level 4-digit SIC regulated firms

(1) (2) (3) (4)

Panel A: OLS estimates

Decentralization 0.4961 0.0660∗ -0.0300 -0.0404(0.3328) (0.0375) (0.0392) (0.0296)

Competitor decentralization 0.1430∗∗∗ 0.0150 -0.0415∗∗∗ -0.0250∗

(0.0503) (0.0099) (0.0124) (0.0125)R2 0.1527 0.9790 0.9839 0.9859

Panel B: IV estimates

Decentralization -1.9649 0.3617∗∗ 0.2813 0.0802(4.2489) (0.1685) (0.1713) (0.1509)

Competitor decentralization 0.5077∗ -0.0213 -0.0275 -0.0499(0.2930) (0.0442) (0.0440) (0.0379)

F statistic 0.4891 0.9339 0.9948 0.9041Overid. p-value 0.0943 0.2562 0.3655 0.7568

State FE N Y Y YYear FE N N Y YEconomic controls N N N YObservations 816 816 816 816

Notes: The dependant variable is the logarithm of the number of 4-digit SIC establishments in a state.Economic controls include the logarithm of state GDP and population, for the state and competitorstates. Robust standard errors, adjusted for clustering at the state-level, appear in parentheses. ∗, ∗∗,and ∗∗∗ denote estimates different from zero at the 10%, 5%, and 1% significance levels.

Focusing first on the OLS estimates in panel (A), column (1) includes only the decentral-

ization measure for the state and the sum of decentralization measures for competitor states.

The first row reports the effect of decentralization on the state’s own firms. The coefficient

estimate, equal to 0.4961, implies that a one-standard-deviation increase in decentralization

40Table B.2 in the Appendix reports estimates using 2-digit SIC establishments as the definition of anaffected establishment. Tables B.3 and B.4 report estimates of the effect of decentralization on county-level 4-digit and 2-digit SIC establishment counts, respectively, that also include county fixed effects. Afterincorporating county-level controls, the number of observations drops because I restrict the sample to 1977and later. The reason is because I include indicators for whether the county fell into non-attainment statusunder NAAQS. As Chay and Greenstone (2005) document, the pre-1977 designation of non-attainmentcounties is somewhat unreliable compared to 1977 and later. The estimates in all of these tables are allsimilar to those reported in Table 5.

31

increases the count of potentially regulated establishments by 20%, which seems implausibly

high. The second row reports the coefficient estimate for competing states’ decentralization,

which is estimated to equal 0.1430. The sign of the estimate should be the opposite to that

of the own-state effect if interjurisdictional policy differences shift firm locations. Instead,

the coefficient estimate is positive, and both estimates are of the same sign.

The estimates in column (1) are likely biased because of omitted variables. After control-

ling for state fixed effects, in column (2), and adding year fixed effects, in column (3), the

estimates are much lower in magnitude. Column (4) is the full specification, which includes

state and year fixed effects as well as the economic controls. The coefficient estimate in

column (4) for the own-state effect is negative, as is the competitor decentralization effect.

Both parameter estimates are biased toward zero though; if decentralization does weaken

regulation compared to the EPA’s enforcement, then the own-state effect should be positive,

and the estimate in (4) is lower in value than the causal effect. The competitor decentraliza-

tion estimate of −0.0250 implies that a one-standard-deviation increase in decentralization

by a single competing state decreases the number of affected firms in the state by about 1%.

If the true effect of competitor decentralization is negative, then this OLS estimate is an

upper bound on the true value of the causal effect.

The IV estimates reported in panel (B) of Table 5 are directly comparable to the same-

column OLS estimates from panel (A). After controlling for state fixed effects, in column (2),

the IV estimates for the state’s own decentralization measure are positive and larger in value

than the corresponding OLS estimate. Column (4) reports the coefficient estimate based

on the full specification to be equal to 0.0802. This estimate implies that a one-standard-

deviation increase in decentralization increases the number of affected establishments in

that state by 3.3%, which is approximately 3.5% of one standard deviation in the firm count

measure. This estimate indicates that following a transfer of regulatory authority from the

EPA to a given state, the number of firms increases in that state, all else being equal, implying

that decentralization leads to an economic benefit from increasing firms there. Furthermore,

if firms are mobile and locate in states where regulation is least-costly, then this evidence

suggests that state enforcement of the standards is less costly to regulated firms than EPA

enforcement.

If firms respond to interjurisdictional policy differences that ultimately lead to a compe-

tition for firms, the effect of competing states’ decentralization measures should be opposite

in sign to the effect of the own-state decentralization choice. After accounting for state

fixed effects in column (2), the coefficient estimate for competitor decentralization is indeed

32

negative in each column and, compared to the same-column OLS estimate, larger in mag-

nitude. The estimate in column (4), estimating from employing the full specification, is

equal to −0.0499 and implies that a one-standard-deviation increase in a single competing

state’s decentralization measure decreases the count of regulated establishments in the state

by just over 2%. This implies that if a competing state undertakes decentralization, this will

decrease firms in the state in question; by increasing its own decentralization by nearly the

same amount, the state in question can counteract this effect to maintain (or increase) the

number of firms there.

Taken together, the IV estimates imply that decentralization causes the number of reg-

ulated establishments to increase in a state, while decentralization to competing states has

the opposite effect. These estimated elasticities to interjurisdictional policy differences are

necessary for states to be able engage in a competition for firms. Furthermore, this evi-

dence corroborates the ‘strategic complementarity’ argument based on the reaction function

estimates: firms respond to interjurisdictional policy differences, and if one state seeks de-

centralization to increase firms in its state, the state that loses firms is induced into seeking

decentralization to counteract this competitive effect.

4.4 Estimates of the pollution function

To estimate the effect of decentralization on TSP concentrations in a state, I augment the

pollution equation (1) by including the year index t. The TSP concentration equation is

then given by

pjt = β0 + β1djt + β2njt + β41pjtd−jt + β51

pjtn−jt + wjtβ5 (7)

+1pjtw−jtβ6 + ZjtΦ + κj + κt + ξjt,

where most of the variables are the same as in equation (1).41 Again, the matrix Zjt includes

the logarithm of state-level real GDP and the logarithm of state population for the state and

its upwind neighbors. The parameters κj and κt are state and year fixed effects, respectively.

Table 6 reports OLS coefficient estimates of the decentralization and firm count variables

for the state in question and upwind states from equation (7).42 Column (1) includes only

41Since TSP has a short atmospheric lifetime, there is no lag dependence in the specification. Includinglag dependence is important for longer-lived pollutants.

42Table B.5 reports estimation results performed by the same analysis, but at the county-level with county,instead of state, fixed effects and additional county-level controls. The results are virtually the same as theestimates reported in this section from using state-level data.

33

the decentralization measure and the logarithm of the count of potentially regulated 4-digit

SIC establishments, for the state in question and upwind states. Each column following

column (1) adds progressively more controls.

The estimate in column (1) of the effect of a state’s decentralization choice on its own TSP

concentration, −0.1523, implies that a one-standard-deviation increase in decentralization in

a state decreases local TSP concentrations by about 6% on average. The coefficient estimate

for a state’s count of regulated firms indicates that a 1% increase in the number of regulated

establishments increases TSP concentrations by 0.08% on average. The coefficient estimate

in the third row is equal to −0.0894 and implies that a one-standard-deviation increase in

decentralization to a single, upwind state decreases pollution in the downwind state by about

3.7%. The coefficient estimate in the final row indicates that a 1% increase in the number

of firms in a single upwind state increases TSP in the state in question by about 0.01% on

average. In terms of consistency, it makes sense that the firm count in a state increases

pollution there and the effect of upwind firms is the same sign but smaller in magnitude,

due to transboundary pollution. Similarly, the effects of decentralization to a state and to

an upwind state are the same sign, though negative, with the latter of smaller magnitude

than the former.

Table 6: The effect of decentralization on state TSP concentrations

(1) (2) (3) (4) (5)

Decentralization -0.1523∗∗∗ -0.1172∗∗∗ 0.0006 0.0024 -0.0003(0.0407) (0.0253) (0.0339) (0.0336) (0.0336)

Log(firm count) 0.0821∗∗∗ 0.0848 0.1567∗∗ 0.1548∗∗ 0.0981∗∗

(0.0292) (0.0586) (0.0591) (0.0584) (0.0409)Upwind decentralization -0.0894∗∗ -0.1004∗∗∗ -0.0480∗∗ -0.0532∗∗ -0.0478∗∗

(0.0346) (0.0253) (0.0229) (0.0226) (0.0229)Upwind log(firm count) 0.0113∗ 0.0075∗ 0.0014 0.0045 -0.0101

(0.0061) (0.0039) (0.0033) (0.0055) (0.0130)R2 0.1797 0.1713 0.2081 0.1943 0.1511

State FE N Y Y Y YYear FE N N Y Y YWeather controls N N N Y YEconomic controls N N N N YObservations 803 803 803 803 803

Notes: The dependant variable is the logarithm of state-level annual TSP concentration (µg/m3).Weather controls include surface temperature and temperature difference, for the state and the upwindstates. Economic controls include the logarithm of state GDP and population, for the state and upwindstates. Robust standard errors, adjusted for clustering at the state-level, in parentheses. ∗, ∗∗, and ∗∗∗

denote estimates different from zero at the 10%, 5%, and 1% significance levels.

34

Though the results in column (1) seem consistent, they are likely biased cause of omitted

variables. After including state fixed effects, in column (2), year fixed effects in column

(3), and weather and economic controls in columns (4) and (5), respectively, the coefficient

estimates change in sign and magnitude. In the full specification, column (5), the coefficient

estimate for decentralization has a negligible effect on TSP concentrations, while the effect

of a state’s count of firms has a similar effect in column (5) to all previous specifications.

The effect of upwind decentralization remains negative, though is approximately have the

value of the estimate in column (1). The estimated effect of the count of upwind firms is

negative, which is unreasonable from a causal perspective.

Of course, the OLS estimates in Table 6 cannot be interpreted as causal. As the model

in Section 2 predicts, the OLS estimates of the effect of a state’s decentralization on its

pollution will be biased toward zero, as will be an upwind state’s decentralization effect on

the state in question’s TSP. The causal effects could be either positive or negative. Though

the effects of a state’s and upwind states’ firms on TSP are, conditional on decentralization

and upwind decentralization, not correlated with the errors, the bias on decentralization and

the correlation of decentralization with the firm count variables (as estimated in the previous

subsection) would bias the coefficients on the firm counts as well.

Taking into account the estimated effects of decentralization on firm counts, it is possible

that decentralization could have a negative effect on TSP concentrations. This could occur

if, for instance, states did have a better understanding of local tastes and needs in the

sense of providing cost-effective enforcement of the standards relative to the EPA. It may be

more likely, however, that states are counteracting upwind state decentralization by avoiding

decentralization – as suggested by the estimates for the reaction function – to not exacerbate

increase in local pollution following decentralization to upwind states. In equilibrium, such

behaviour could be seen to reduce pollution, although the causal effects of decentralization

are actually positive. Without a way to estimate those causal effects, the equilibrium effects

reported in Table 6 do not shed light on what those causal effects may look like. By altering

the definition of states that can generate pollution spillovers, the following section may shed

some insight on what these causal effects may actually look like.

5 Alternative specifications for spillovers

The previous section categorized competition as occurring between adjacent states and trans-

boundary pollution as occurring between adjacent states in the direction of the wind. This

35

assumption is conservative: both the mean and median distance between state geographic

centroids is about 435 kilometers, while the mean and median distance between all states

is about 1400 and 1300 kilometers, respectively. To evaluate the robustness of the baseline

estimates, I consider alternative specifications of spillovers based on distance. In particu-

lar, I first consider varying competition to occur between states whose geographic centroids

are no more than 500, 1000, 1500, and 2000 kilometers apart, while keeping transboundary

pollution restricted to occur between adjacent states in the direction of the wind. Second,

I keep competition fixed to occur between adjacent states, while varying the specification

of transboundary pollution to occur between states that are 500, 1000, 1500, and 2000

kilometers apart in the direction of the wind. Given that competition and transboundary

pollution likely occurs within small distances from a state’s centroid, the smallest and largest

ranges likely misspecify the relationships between states and cause the results to change in

predictable ways, while the middle distances yield estimates that are more reasonable.

I first consider changing the definition of competition, while restricting transboundary

pollution to still occur between adjacent states in the direction of the wind. Table 7 reports

OLS and IV coefficient estimates for the variables of interest from the reaction function and

the firm count equation.43 All columns report estimates from employing the full regression

specification for that respective dependent variable.

The first two columns of Table 7a report coefficient estimates for the reaction function

when competition is restricted to occur between states whose geographic centroids are no

more than 250 kilometers from one another. Because this short distance omits some con-

tiguous neighbors, this specification includes states that are defined as upwind (and thus

potentially generating pollution spillovers) that are not competitors to the state in question.

The difference between reported OLS and IV estimates reveal that the OLS estimates are

biased in the same predicted way that the OLS baseline estimates are, as reported in Table 4.

The IV estimate for non-upwind states, in the first row, implies that states that are defined

exclusively as competitors cause the state in question to mimic their decentralization choices,

just as in the baseline specifications. For states that can only generate transboundary pol-

lution and do not compete, the third row, undertaking decentralization causes the state in

question to do the opposite, a result consistent with the free-riding incentives caused by

transboundary pollution. States that are defined as competitors and who generate pollution

spillovers, the second row, predictably have an effect that falls between the effects of the

43The definition of competition does not affect the specification of the pollution equation, so there areno estimates to report for the effect of decentralization on TSP concentrations that are different than thosereported in Table 6.

36

Tab

le7:

Var

yin

gth

edis

tance

bet

wee

nco

mp

etin

gst

ates

(a)

Est

imat

esof

the

reac

tion

fun

ctio

n

Com

pet

itio

nfa

lls

wit

hin

500

km

1000

km

1500

km

2000km

OL

SIV

OL

SIV

OL

SIV

OL

SIV

Non

-upw

ind

com

pet

itor

dec

entr

aliz

atio

n0.

0087

0.3

427∗∗

0.0

257∗

∗0.0

991∗

0.0

174∗

∗∗0.0

582∗

∗0.0

134∗

∗0.0

261

(0.0

196)

(0.1

361)

(0.0

097)

(0.0

538)

(0.0

064)

(0.0

254)

(0.0

062)

(0.0

262)

Upw

ind

com

pet

itor

dec

entr

aliz

atio

n0.

1154

0.1

830

0.0

455

0.7

900∗

∗0.0

431

0.0

776

0.0

668

-0.0

226

(0.0

695)

(0.7

608)

(0.0

480)

(0.3

285)

(0.0

469)

(0.4

202)

(0.0

463)

(0.5

164)

Upw

ind

non

-com

pet

itor

dec

entr

aliz

atio

n0.

0479

-0.6

471

(0.0

468)

(1.5

354)

Not

es:

Th

ed

epen

dan

tva

riab

leis

the

dec

entr

aliz

ati

on

mea

sure

.A

llsp

ecifi

cati

on

sin

clu

de

state

an

dye

ar

fixed

effec

tsan

dth

eli

stof

contr

ols

inT

able

4.R

obu

stst

and

ard

erro

rs,

adju

sted

for

clu

ster

ing

at

the

state

-lev

el,

inp

are

nth

eses

.∗ ,

∗∗,

an

d∗∗

∗d

enote

esti

mate

sd

iffer

ent

from

zero

atth

e10

%,

5%,

and

1%

sign

ifica

nce

leve

ls.

(b)

Est

imat

esof

the

effec

tof

dec

entr

aliz

atio

non

4-d

igit

firm

cou

nts

Com

pet

itio

nfa

lls

wit

hin

500

km

1000

km

1500

km

2000km

OL

SIV

OL

SIV

OL

SIV

OL

SIV

Dec

entr

aliz

atio

n-0

.063

2∗

-0.0

597

-0.0

501

0.0

578

-0.0

410

0.1

182

-0.0

423

0.1

062

(0.0

353)

(0.0

690)

(0.0

377)

(0.0

575)

(0.0

349)

(0.2

072)

(0.0

312)

(0.1

789)

Com

pet

itor

dec

entr

aliz

atio

n0.

0103

-0.0

082

-0.0

013

-0.0

276

-0.0

038

-0.0

284

-0.0

085∗

∗-0

.0377∗∗

(0.0

131)

(0.0

288)

(0.0

051)

(0.0

214)

(0.0

031)

(0.0

199)

(0.0

033)

(0.0

190)

Not

es:

Th

ed

epen

dan

tva

riab

leis

the

loga

rith

mof

the

nu

mb

erof

4-d

igit

SIC

esta

bli

shm

ents

ina

state

.A

llsp

ecifi

cati

on

sin

clu

de

state

and

year

fixed

effec

tsan

dth

eli

stof

contr

ols

inT

ab

le5.

∗ ,∗∗

,an

d∗∗

∗d

enote

esti

mate

sd

iffer

ent

from

zero

at

the

10%

,5%

,an

d1%

sign

ifica

nce

level

s.

37

other two types of states. Overall, these estimates are quite similar to the baseline estimates

reported in Table 4.

As one moves across the columns, a few patterns emerge.44 First, the OLS estimates

for non-upwind decentralization become smaller in magnitude the larger is the range over

which competition occurs. This attenuation likely occurs because the range includes distant

states with which a state does not compete. In terms of the IV estimates for non-upwind

decentralization, these too are smaller in magnitude than the estimates based on contiguity-

based competition, likely because competition is misspecified for such large distances. Note

that the IV estimates for upwind competitor decentralization are greater in value than the

estimate for non-upwind decentralization for distances of 1000 and 1500 kilometers, then

changes sign for 2000 kilometers. This fluctuation in the coefficient estimate arises because

there are fewer instruments for upwind states: in the contiguity-based definitions of compe-

tition and pollution, an upwind state’s upwind neighbor had no relationship with the state

in question. In expanding definitions of competition, an upwind state’s upwind neighbor is

also the competitor to the state in question, resulting in relatively poor variation to identify

the effect of the upwind state’s decentralization.

Table 7b reports estimates of the firm count function for the different distance-based

definitions of competition.45 Except for the 500 kilometer range, which is probably too

restrictive a definition for competition, all of the coefficient estimates for a state’s own

decentralization and competing states’ decentralization are the expected and same sign as

those reported in Table 5. The IV estimates in particular are close in magnitude to the

contiguity-based estimates for distances of 1000 and 1500 kilometers. Given that competition

between states would most likely occur for these distance ranges, these estimates support

the baseline estimates reported in the previous section.

Next, I consider varying the range over which transboundary pollution occurs, while

keeping competition as occurring only between adjacent states. Table 8 reports coefficient

estimates for the reaction function and the firm count function for various distance-based

specifications of transboundary pollution. The estimates for the reaction function, in Table

8a, reveal a similar pattern to the set of results reported so far in this paper. States that are

defined exclusively as competitors cause the state in question to mimic their decentralization

44Note that distances of 1000 kilometers or more include all adjacent states. For such distances, statesthat are upwind of a given state are also competitors to that state. As a result, there is no estimate to reportfor upwind non-competitor decentralization.

45Table B.6a in the Appendix reports estimates for the same specifications, but at the county-level thatinclude county-level controls and fixed effects. Similarly, Table B.6b reports estimates for the same county-level specifications for different ranges of transboundary pollution.

38

Tab

le8:

Var

yin

gth

edis

tance

for

tran

sbou

ndar

yp

ollu

tion

(a)

Est

imat

esof

the

reac

tion

fun

ctio

n

Tra

nsb

oun

dar

yp

ollu

tion

fall

sw

ith

in500

km

1000

km

1500

km

2000km

OL

SIV

OL

SIV

OL

SIV

OL

SIV

Non

-upw

ind

com

pet

itor

dec

entr

aliz

atio

n0.

0705∗∗

∗0.2

676∗

0.0

707∗

∗∗0.2

182∗

∗0.0

685∗

∗∗0.1

529

0.0

680∗∗

∗0.1

705∗

(0.0

218)

(0.1

517)

(0.0

217)

(0.0

949)

(0.0

213)

(0.1

094)

(0.0

210)

(0.0

969)

Upw

ind

com

pet

itor

dec

entr

aliz

atio

n0.

1087∗

-1.2

821

0.0

637

0.0

986

0.0

712∗

-0.9

128∗∗

0.0

768∗

-0.4

504

(0.0

605)

(2.8

601)

(0.0

425)

(0.3

480)

(0.0

418)

(0.4

635)

(0.0

414)

(0.6

020)

Upw

ind

non

-com

pet

itor

dec

entr

aliz

atio

n-0

.247

9∗∗∗

-1.4

332

-0.0

004

-0.0

339

-0.0

128

0.1

365

-0.0

092

-0.0

038

(0.0

665)

(3.5

268)

(0.0

338)

(0.2

380)

(0.0

196)

(0.2

329)

(0.0

128)

(0.0

587)

Not

es:

Th

ed

epen

dan

tva

riab

leis

the

dec

entr

aliz

ati

on

mea

sure

.A

llsp

ecifi

cati

on

sin

clu

de

state

an

dye

ar

fixed

effec

tsan

dth

eli

stof

contr

ols

inT

able

4.R

obu

stst

and

ard

erro

rs,

adju

sted

for

clu

ster

ing

at

the

state

-lev

el,

inp

are

nth

eses

.∗ ,

∗∗,

an

d∗∗

∗d

enote

esti

mate

sd

iffer

ent

from

zero

atth

e10

%,

5%,

and

1%

sign

ifica

nce

leve

ls.

(b)

Est

imat

esof

the

effec

tof

dec

entr

aliz

atio

non

4-d

igit

firm

cou

nts

Tra

nsb

oun

dar

yp

ollu

tion

fall

sw

ith

in500

km

1000

km

1500

km

2000km

OL

SIV

OL

SIV

OL

SIV

OL

SIV

Dec

entr

aliz

atio

n-0

.063

2∗-0

.0890

-0.0

501

-0.0

871

-0.0

410

-0.0

411

-0.0

423

0.0

384

(0.0

353)

(0.0

603)

(0.0

377)

(0.0

993)

(0.0

349)

(0.1

180)

(0.0

312)

(0.1

400)

Com

pet

itor

dec

entr

aliz

atio

n0.

0103

0.0

585

-0.0

013

-0.0

010

-0.0

038

0.0

047

-0.0

085∗∗

-0.0

322

(0.0

131)

(0.0

468)

(0.0

051)

(0.0

160)

(0.0

031)

(0.0

123)

(0.0

033)

(0.0

333)

Not

es:

Th

ed

epen

dan

tva

riab

leis

the

loga

rith

mof

the

nu

mb

erof

4-d

igit

SIC

esta

bli

shm

ents

ina

state

.A

llsp

ecifi

cati

on

sin

clu

de

state

and

year

fixed

effec

tsan

dth

eli

stof

contr

ols

inT

ab

le5.

∗ ,∗∗

,an

d∗∗

∗d

enote

esti

mate

sd

iffer

ent

from

zero

at

the

10%

,5%

,an

d1%

sign

ifica

nce

level

s.

39

choices, whereas states that cause pollution spillovers may induce the state in question to do

the opposite of what they do. The coefficient estimates, particularly for the upwind states,

are unexpectedly larger in value than the estimate for non-upwind decentralization for the

500 kilometer range, likely because this range for transboundary pollution is too restrictive.

For larger distance ranges, the coefficient estimates for upwind states fluctuate, most likely

because transboundary pollution is prohibitive for the upper end of those ranges.

The coefficient estimates for the firm count function for varying ranges of transboundary

pollution are reported in Table 8b. For the most part, both OLS and IV coefficient estimates

are the same as those reported in Table 5, though the estimates do fluctuate more than when

the specification for competition is expanded. In comparison, county-level estimates of the

firm count function based upon varying definitions of transboundary pollution, reported in

Table B.6b in Appendix B, are much more robust.

Table 9: Estimates of the effect of decentralization on TSP concentrations, by differentspatial distances for transboundary pollution

Transboundary pollution falls within 500 km 1000 km 1500 km 2000 km

Decentralization -0.0066 -0.0053 -0.0040 -0.0037(0.0344) (0.0354) (0.0354) (0.0354)

Log(firm count) 0.1033∗∗∗ 0.1017∗∗ 0.1032∗∗ 0.1064∗∗∗

(0.0385) (0.0402) (0.0404) (0.0394)Upwind decentralization -0.0178 0.0083 0.0054 0.0035

(0.0424) (0.0218) (0.0132) (0.0086)Upwind log(firm count) 0.0002 -0.0008 -0.0012 0.0016

(0.0140) (0.0073) (0.0041) (0.0041)

Notes: The dependant variable is the logarithm of the ambient air TSP concentrations in a state andyear. All specifications include state and year fixed effects and the list of controls in Table 6. ∗, ∗∗, and∗∗∗ denote estimates different from zero at the 10%, 5%, and 1% significance levels.

Finally, Table 9 reports OLS estimates of the pollution equation (7) for different ranges

of potential transboundary pollution.46 The coefficient estimates of the equilibrium effect of

a state’s own decentralization choice are similar in sign and magnitude to those reported in

Table 6. Notably, the equilibrium effect of upwind decentralization changes sign to positive

for ranges of 1000 kilometers or larger. In particular, for transboundary pollution occuring

between states that are no more than 1000 kilometers apart, the coefficient estimate indicates

that a transfer of regulatory authority to a single upwind state is associated with a TSP

46Table B.7 in the Appendix reports estimates for the same specifications, but at the county-level thatinclude county-level controls and fixed effects.

40

increase in the downwind state by nearly 1% on average. Similarly, for distances of 1500 and

2000 kilometers, the effect is estimated to equal about 0.5% and 0.35%, respectively. Taken

together with the baseline estimates, as well as those reported in this section, this evidence

suggests that decentralization to a given state: (1) increases the count of firms in that state

and decreases the count of firms in competing states, and; (2) increases the concentration

of ambient TSP in downwind states. With the reaction function estimates, this evidence

suggests that states undertake decentralization to provide less costly regulation to regulated

firms, thereby attracting mobile firms and, at least in downwind states, worsen air quality

through interstate pollution, and that states in part do this strategically because of these

spillovers.

Overall, the estimates reported in this section, based on different distance-based defi-

nitions of competition and transboundary pollution, support the baseline estimates for the

reaction function, firm count equation, and pollution equation reported above. Addition-

ally, these distance-based results highlight the importance of specification when spillovers

between states are likely to occur. For implausibly restrictive or expansive specifications of

who affects whom, the results can become predictably unreasonble. Therefore, this evidence

suggests that it is crucial to properly specify how competition and transboundary pollution

can arise when estimating the effect of interjurisdictional policies on firms and pollution.

6 Conclusion

This paper seeks to evaluate the effects of decentralizing air pollution regulation to local

governments. I do this by first developing a model of interjurisdictional regulation-setting

for air pollution control, where regulation affects not only air pollution and regulated firms at

home, but also in other jurisdictions through competition for mobile firms and transboundary

pollution. The model produces three estimable equations: the effect of jurisdictions’ policies

on a given jurisdiction’s count of firms and air pollution level, as well as a reaction function

specifying how each jurisdiction sets its regulatory policy. The benefit of this integrated

model is that it is clear that plausible exclusion restrictions based on the mechanics of air

pollutant transport serve to identify most of the model. In particular, I show that if the

pollutant is a regional, as opposed to global, pollutant, then not every jurisdiction affects

every other jurisdiction through transboundary pollution. These exclusion restrictions imply

that local and exogenous meteorological conditions that disperse air pollution locally can

be used as instruments for the endogenous regulation choices of the local jurisdiction and

41

potentially for other jurisdictions’ endogenous regulations as well. Further, this result implies

that, instead of having to rely on natural experiments that are difficult to find, identifying

and measuring the causal effect of endogenous environmental regulation on several important

outcomes can be realized by using publicly available data on weather patterns.

To estimate the effect of decentralization on firm counts, air pollution, and other juris-

dictions’ decentralization choices, I use a unique dataset on decentralization of enforcement

authority over industry-specific total suspended particulates (TSP) emissions standards from

the Environmental Protection Agency (EPA) to willing states during the period 1971–1990.

I find that states choose the degree of regulatory decentralization they undertake in part

by mimicking what states with whom they compete for mobile industries do, so that states’

decentralization choices are ‘strategic complements.’ I also find evidence of ‘strategic sub-

stitution,’ whereby increased decentralization to upwind states cause the downwind state to

not seek regulatory authority. Though, as Ogawa and Wildasin (2009) predict, I find that

these strategic motives can partly offset one another, the fact that not all states incur both

types of spillover means that policy distortions remain and that, as a whole, decentralization

of regional air pollutants in the U.S. is likely inefficient.

I also find that decentralization to a given state causes the number of potentially regu-

lated establishments to increase in that state and the number of regulated establishments in

a competing state to decrease. This evidence implies that mobile firms do respond to inter-

jurisdictional policy differences, a finding that has proved somewhat elusive in the literature,

with notable exceptions being ? and Duranton et al. (2011). Together with the reaction

function estimates that states mimic what competing states choose, this evidence supports

the notion that U.S. states use their decentralization choices to engage in a ‘race’ to compete

for mobile firms.

I find that the equilibrium effect of decentralization on TSP concentrations is negligibly

small or negative. For distances between states of 1000 kilometers, I find that the equilibrium

effect of decentralization to an upwind state increases TSP concentrations in the downwind

state by nearly 1% on average. This finding is consistent with the notion that states will not

account for transboundary pollution when setting regulations, thereby increasing pollution

in nearby jurisdictions, and is a finding that echoes findings in Sigman (2002) and Sigman

(2005).

The evidence in this paper presents a negative, though long ago predicted, view of air

pollution regulation. Taken together with the reaction function and firm count estimates,

this evidence implies that states that undertake decentralization reduce the cost of regulation

42

to firms, thereby increasing firms there and decreasing them in competing states, while also

increasing air pollution downwind. These two effects distort states’ decentralization choices,

in some cases exacerbating the magnitude of spillovers generated upon other states. Because

of the evidence in support of a ‘race to the bottom’ and transboundary pollution quantified

in this paper, it is likely the case that decentralized regulation of air pollution, at least in

the U.S. and for the regulation of total suspended particulates, is inefficient.

43

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46

Appendix

A Figures

Federal Register / Vol. 47, No. 74 / Friday, April 16, 1982 1 Notices

of this disposition in the FederalRegister. If EPA grants a test marketingexemption, it may impose restrictions onthe test marketing activities.

On March 3, 1982, EPA received anapplication for an exemption from therequirements of sections 5(a) and 5(b) ofTSCA to import a new chemicalsubstance for test marketing purposes.The application was assigned testmarketing exemption number TM-82-7.The importer claimed his identity, thespecific chemical identity, and thespecific use of the new substance asconfidential business information. Thegeneric name of the new substance is4,4' thio diether dianhydride, and it willbe used in specialty adhesives. Amaximum of 500 pounds will beimported for test market purposes,during a test marketing period not toexceed 18 months. During processing,four workers will be dermally exposed 1hour a day for a total of 72 hours. Anotice published in the Federal Registerof March 12, 1982 (47 FR 10899)announced receipt of this applicationand requested comment on theappropriateness of granting theexemption. The Agency has not receivedany comments concerning theapplication.

EPA has established that the testmarketing of the substance described inTM-82-7, under the conditions set out inthe application, will not present anyunreasonable risk of injury to health orthe environment. Although there weresome human health and environmentalconcerns for this substance, they weremitigated by low worker exposure andenvironmental release. The TMEsubstance will be processed in a closedsystem. There will be no consumerexposure.

This test marketing exemption isgranted based on the facts andinformation obtained and reviewed, butis subject to all conditions set out in theexemption application and, in particular,those enumerated below.

1. This exemption is granted solely tothis importer.

2. Each bill of lading that accompaniesa shipment of the substance during thetest marketing period must state that theuse of the substance is restricted to thatdescribed to EPA in the test marketingexemption application.

3. The production volume of the newsubstance may not exceed the quantityof 500 pounds described in the testmarketing exemption application.

4. The test marketing activityapproved in this notice Is limited to aperiod of 18 months commencing on thedate of signature of this notice by theAdministrator.

5. The number of workers exposed tothe new chemical should not exceed

'that specified in the application and theduration of exposure should not exceedthat specified.

The Agency reserves the right torescind its decision to grant thisexemption should any new informationcome to its attention which castssignificant doubt on the Agency'sconclusion that the test marketing of thissubstance under the conditions specifiedin the application will not present anunreasonable risk of injury to humanhealth or the environment.

Dated: April 5,1982.Anne M. Gorsuch,.Administrator.[FR Doc. 82-10328 Filed 4-15-82; 8:45 am]

BILUNG CODE 6560-50-M

[A-2-FRL-2103-1]

Standards of Performance For NewStationary Sources and NationalEmission Standards for Hazardous AirPollutants; Delegation of Authority tothe State of New YorkAGENCY: Environmental ProtectionAgency.ACTION: Notice of delegation ofauthority.

SUMMARY: This notice announces thedelegation of authority by theEnvironmental Protection Agency to theState of New York, per the New YorkState Department of EnvironmentalConservation, to implement and enforceportions of the Standards ofPerformance for New Stationary.Sources codified at 40 CFR Part 60, andportions of the National EmissionStandards for Hazardous Air Pollutantscodified at 40 CFR Part 61.

This delegation is implementedpursuant to sections 111(e) and 112(d) ofthe Clean Air Act and a request madeby the State. After a thorough review ofthe request and information submittedby the State of New York, the RegionalAdministrator has determined that suchdelegation is appropriate for the sourcecategories requested and with theconditions set forth in the delegationagreement.EFFECTIVE DATE: This action is effectiveApril 16, 1982.FOR FURTHER INFORMATION CONTACT.Francis W. Giaccone, Chief, AirFacilities Branch, Air and WasteManagement Division, EnvironmentalProtection Agency, Region II Office, 26Federal Plaza, New York, New York10278, (212] 264-9627.SUPPLEMENTARY INFORMATION: On July15, 1981, Commissioner Robert F. Flacke

of the New York State Department ofEnvironmental Conservation (DEC)requested that the EnvironmentalProtection Agency (EPA) delegate tothat Department the authority toimplement and enforce certain portionsof the federally established Standards ofPerformance for New StationarySources (NSPS) and National EmissionStandards for Hazardous Air Pollutants(NESHAPS) as follows:

NSPS (40 CFR 60 Subpart)E IncineratorsF Portland Cement PlantsG Nitric AcidPlantsG Sulfuric Acid PlantsI Asphalt Concrete PlantsJ Petroleum RefineriesK Storage Vessels for Petroleum LiquidsKa Storage Vessels for Petroleum Liqids

constructed after 5/16/78L Secondary Lead SmeltersM Secondary Brass & Bronze Ingot

ProductionN Iron & Steel Plants0 Sewage Treatment PlantsP Primary Copper SmeltersQ Primary Zinc SmeltersR Primary Lead SmeltersS Primary Aluminum Reduction PlantsT Wet Process Phosphoric Acid PlantsU Superphosphoric Acid PlantsV Di-Ammonium Phosphate PlantsW Triple Superphosphate PlantsX Granular Triple Superphosphate PlantsY Coal Preparation PlantsZ Ferroalloy Production FacilitiesAA Electric Arc Furnaces in the Steel

IndustryBB Kraft Pulp MillsCC Glass Manufacturing PlantsDD Grain ElevatorsHH Lime Manufacturing PlantsMM Automobile & Light-Duty Truck Surface

Coating OperationsPP Ammoniun Sulfate Manufacture

NESHAPS (40 CFR 61 Subpart)B Asbestos [excepting Sections 61.22(b)

(Surfacing of Roadway with Asbestos-Containing Materials), 61.22(d)(Demolitions and Renovations), 61.22(i)(Insulating), and 61.22(j)(2), 61.22(k)(2), and61.25 (all relating to waste disposal)].

C BerylliumD Beryllium Rocket Motor FiringE MercuryF Vinyl Chloride

EPA is also, pursuant to the State's July15,1981 request, withdrawing itsdelegation of authority to implement andenforce 40 CFR 61.22(b), Surfacing ofRoadways with Asbestos.

In previous correspondence with theCommissioner and with the GeneralCounsel of the DEC, the authority andprocedural mechanisms whereby the-Department can accomplish suchimplementation and enforcement havebeen outlined. Copies of thiscorrespondence and EPA's delegation

16409

Figure A.1: An example of a transfer of enforcement authority published in the FederalRegister (continued)

47

Federal Register / Vol. 47, No. 74 / Friday, April 16, 1982 / Notices

letter are available for public inspectionin the Office of the Air Facilities Branchat the Environmental Protection Agency,Region II Office, 26 Federal Plaza, NewYork, New York 10278.

Section 111(c) of the Clean Air Actdirects the Administrator to delegateEPA's authority to implement andenforce NSPS to any state which hassubmitted adequate procedures. Section112(d)'of the Clean Air Act providessimilar direction with respect toNESHAPS. In both instances, theAdministrator retains concurrentauthority to enforce the standardsfollowing delegation of authority to astate.

EPA's determination that thedelegation request should be approvedis based upon the Agency's review ofthe New York State EnvironmentalConservation Law, Article 19, (AirPollution Control), and Article 71, Title21, (Enforcement of Article 19), andParts 201, 202, and 212 of Title, 6,Official Compilation of Codes, Rulesand Regulations of the State of NewYork.(NYCRR), as presently enacted.Specifically 6 NYCRR Section 201.2provides that no one shall construct oroperate a source of air contamination(as defined at 6 NYCRR section 200.1(d))without having a valid permit toconstruct or certificate to operate issuedby'the DEC. Section 201.4(b) authorizesthe Commissioner of the DEC, in issuingsuch permits or certificates, to "imposesuch conditions as are-necessary toinsure compliance with applicablefederal source standards and reportingrequirements," such as the NSPS andNESHAPS for which the DEC hasrequested delegation. EPA has thereforedetermined that such delegation isappropriate and has so notified the Stateby a letter dated December 30, 1981.This letter identifies the conditionsunder which delegation is made.

Effective immediately, allcorrespondence, reports andnotifications required by NSPS andNESHAPS covered by this delegationshould be submitted to the appropriateregional office of the New York StateDepartment of EnvironmentalConservation or the central office at 50Wolf Road, Albany, New York 12233,(Attention: Division of Air, Bureau ofSource Control).

The Office of Management and Budgethas exempted this action from therequirements of section 3 of ExecutiveOrder 12291.

(Secs. 111 and 112 of the Clean Air Act, asamended (42 U.S.C. 7411 and 7412))

Dated: March 31, 1982.Jacqueline E. Schafer,Regional Administrator, EnvironmentalProtection Agency.

[FR Doc. 83-10473 Filed 4-15-82: 8:45 am

BILLING CODE 6560-0-M

[TSH-FRL-2104-1; OPTS-59085]

Alkylbenzenesulfonic Acid CompoundWith Dialkyl Fatty AminePremanufacture ExemptionApplicationAGENCY: Environmental ProtectionAgency (EPA).ACTION: Notice.

SUMMARY: EPA.may upon applicationexempt any person from thepremanufacturing notificationrequirements of section 5 (a) or (b) of theToxic Substances Control Act (TSCA) topermit the person tdmanufacture orprocess a chemical for test marketingpurposes under section 5(h)(1) of TSCA.Requirements for test marketingexemption (TME) applications, whichmust either be approved or deniedwithin 45 days of receipt,-are discussedin EPA's revised statement of interimpolicy published in the Federal Registerof November 7, 1980 (45 FR 74378). Thisnotice, issued under section 5(h)(6) ofTSCA, announces receiptof oneapplication for an exemption, provides asummary, and requests comments on theappropriateness of granting theexemption.DATE: Written comments by May 3, 1982.ADDRESS: Written comments, identifiedby the documentcontrol number."[OPTS-59085]" and the specific TMEnumber should be sent to: DocumentControl Officer (TS-793), Office ofPesticides and Toxic Substances,Management Support Division,Environmental Protection Agency, Rm.E-401, 401 M Street, SW., Washington,DC 20460.FOR FURTHER INFORMATION CONTACT:David Dull, Acting Chief, Notice ReviewBranch, Chemical Control Division (TS-'794), Office of Toxic Substances,Environmental Protection Agency, Rm.E-216, 401 M Street, SW., Washington,DC 20460.SUPPLEMENTARY INFORMATION: Thefollowing is a summary of informationprovided by the manufacturer on theTME received by the EPA.

TME 82-13

Close of Review Period. May 21, 1982.Manufacturer. Confidential.Chemical. (G) Alkylbenzenesulfonic

acid compound with dialkyl fatty amine.

Use/Production. (S) Pigment modifier.Prod. range: Six months: 1,090-5,000 kg.

Toxicity Data. No data submitted.Exposure. Manufacture: inhalation, 6

workers, 8 hrs/da, 30 da/yr.En vironmental Release/Disposal.

Less than 10 kg/yr released to air,between 10-100 kg/yr to water(discharge to publicly owned treatmentworks after pH adjustment).

Dated: April 2, 1982.Woodson W. Bercaw,Acting"Director. Management SupportDivision.1FR Doc. 82-10508 Filed 4-15-2: 8:45 am

BILLING CODE 6560-31-M

FEDERAL PREVAILING RATEADVISORY COMMITTEE

Open Committee Meetings

Pursuant to the provisions of section10 of the Federal Advisory CommitteeAct (Pub. L. 92-463), notice is herebygiven that meetings of the FederalPrevailing Rate Advisory Committeewill be held on:

Thursday, May 6, 1982Thursday, May 13, 1982Thursday, May 27, 1982These meetings will convene at 10

a.m., and will be held in Room 5A06A,Office of Personnel ManagementBuilding, 1900 E Street, NW,Washington, D.C.

The Federal Prevailing Rate AdvisoryCommittee is composed of a Chairman.representatives of five labor unionsholding exclusive bargaining .rights forFederal blue-collar employees, andrepresentatives of five Federal agencies.Entitlement to membership of theCommittee is provided for in 5 U.S.C.5347.

The Committee's primaryresponsibility is to review the prevailingrate system and other matters pertinentto the establishment of prevailing ratesunder subchapter IV, chapter 53, 5U.S.C., as amended, and from time totime advise the Office of PersonnelManagement thereon.

These scheduled meetings willconvene in open session with both laborand management representativesattending. During the meeting either thelabor members or the managementmembers may caucus separately withthe Chairman to devise strategy andformulate positions. Prematuredisclosure of the matters discussed inthese caucuses would impair to anunacceptable degree the ability of theCommittee to reach a consensus on thematters being considered and disruptsubstantially the disposition of its

16410

Figure A.1: An example of a transfer of enforcement authority published in the FederalRegister (continued)

48

0.2.4.6.81Decentralization measure

1970

1975

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Yea

r

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MA

NH

RI

VT (a)

Reg

ion

1

0.2.4.6.81Decentralization measure

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ion

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0.2.4.6.81Decentralization measure

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0.2.4.6.81Decentralization measure

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IL IN MI

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0.2.4.6.81Decentralization measure

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0.2.4.6.81Decentralization measure

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0.2.4.6.81Decentralization measure

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0.2.4.6.81Decentralization measure

1970

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ID OR

WA

(h)

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ion

10

Fig

ure

A.2

:D

ecen

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ion

mea

sure

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ions

49

2468Surface temperature (degrees Celsius)

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ion

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4681012Surface temperature (degrees Celsius)

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ion

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-505101520Surface temperature (degrees Celsius)

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-50510Surface temperature (degrees Celsius)

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IL IN MI

MN

OH

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(d)

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246810Surface temperature (degrees Celsius)

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ion

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-50510Surface temperature (degrees Celsius)

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ion

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4681012Surface temperature (degrees Celsius)

1970

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r

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NV (g)

Reg

ion

9

234567Surface temperature (degrees Celsius)

1970

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Yea

r

ID OR

WA

(h)

Reg

ion

10

Fig

ure

A.3

:Surf

ace

tem

per

ature

s(◦

C)

acro

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PA

Reg

ions

50

123456Surface temp - Lower troposphere temp (degrees Celsius)

1970

1975

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Yea

r

CT

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MA

NH

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VT (a)

Reg

ion

1

23456Surface temp - Lower troposphere temp (degrees Celsius)

1970

1975

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Yea

r

NJ

NY (b)

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ion

2

-15-10-50510Surface temp - Lower troposphere temp (degrees Celsius)

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r

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TN (c)

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ion

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-505Surface temp - Lower troposphere temp (degrees Celsius)

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1985

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Yea

r

IL IN MI

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OH

WI

(d)

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ion

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-6-4-202Surface temp - Lower troposphere temp (degrees Celsius)

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Yea

r

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MO

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-6-4-2024Surface temp - Lower troposphere temp (degrees Celsius)

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WY (f)

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-6-4-202Surface temp - Lower troposphere temp (degrees Celsius)

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ion

9

-4-2024Surface temp - Lower troposphere temp (degrees Celsius)

1970

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Yea

r

ID OR

WA

(h)

Reg

ion

10

Fig

ure

A.4

:D

iffer

ence

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nsu

rfac

ean

dlo

wer

-tro

pos

pher

ic(8

50hec

topas

cals

)te

mp

erat

ure

s(◦

C)

acro

ssE

PA

Reg

ions

51

B Tables

Table B.1: Standards and SIC Classifications

Standard 1972 SIC 1987 SIC

D 4911 4911E 4953 4953F 3241 3241I 2951 2951J 2911 2911L 3341 3341M 3341 3341N 3312 3312O 4952 4952AA 3312 3312P 3331 3331Q 3333, 3339 3339R 3332, 3339 3339Y 1210 1221, 1222Z 3313 3313BB 2611 2611DD 4221, 5153 4221,5153HH 3274 3274Da 4911 4911CC 3211,3221,3296,3299 3211,3221, 3296, 3299PP 2873 2873UU 2952 2952AAa 3312 3312LL 1011,1021,1031, 1011,1021,1031,

1041,1044,1061,1099 1041,1044,1061,1099OOO 14 14PPP 3296 3296Db 4911 4911Na 3312 3312AAA 3433 3433

52

Table B.2: The effect of decentralization on the count of 2-digit SIC regulated firms

(1) (2) (3) (4)

Panel A: OLS estimates

Decentralization 0.6361∗∗ 0.0920∗∗∗ 0.0236 0.0043(0.2921) (0.0278) (0.0217) (0.0089)

Competitor decentralization 0.0769 0.0185∗∗∗ -0.0185∗∗ -0.0036(0.0551) (0.0061) (0.0084) (0.0026)

R2 0.1210 0.9934 0.9963 0.9992

Panel B. IV estimates

Decentralization -3.3927 0.1747∗∗ 0.2939∗∗ 0.0711(6.8904) (0.0834) (0.1201) (0.0453)

Competitor decentralization 0.4974 0.0128 -0.0029 -0.0181∗

(0.4940) (0.0196) (0.0394) (0.0095)F statistic 0.4891 0.9339 0.9948 0.9041Overid. p-value 0.6831 0.1892 0.7212 0.9823

State FE N Y Y YYear FE N N Y YEconomic controls N N N YObservations 816 816 816 816

Notes: The dependant variable is the logarithm of the number of 2-digit SIC establishments in a state.Economic controls include the logarithm of state GDP and population, for the state, competitor states,and upwind states. IV estimates from GMM. Robust standard errors, adjusted for clustering at thestate-level, in parentheses. ∗, ∗∗, and ∗∗∗ denote estimates different from zero at the 10%, 5%, and 1%significance levels.

53

Table B.3: The effect of decentralization on the count of county-level 4-digit SIC regulatedfirms

(1) (2) (3) (4)

Panel A. OLS estimates

Decentralization -0.0267 0.0568∗∗ 0.0094 0.0166(0.1268) (0.0234) (0.0172) (0.0174)

Competitor decentralization 0.0175 0.0042 -0.0183∗ -0.0032(0.0308) (0.0053) (0.0095) (0.0063)

R2 0.0007 0.0002 0.0009 0.3625

Panel B. IV estimates

Decentralization 0.2583 0.1926 0.4084 0.1212(0.8004) (0.1682) (0.2745) (0.1561)

Competitor decentralization -0.1027∗ -0.0010 -0.0148 -0.0319(0.0624) (0.0354) (0.0410) (0.0223)

F statistic 163.9217 50.5757 45.8254 45.0606Overid. p-value 0.0142 0.1716 0.8093 0.3397

County FE N Y Y YYear FE N N Y YEconomic controls N N N YObservations 47532 47532 47532 39201

Notes: The dependant variable is the logarithm of the number of 4-digit SIC establishments in a county.Economic controls include the logarithm of state GDP and population, for the state, competitor states,and upwind states, as well as county-level logarithm of real income per capita and population, andNAAQS partial or whole non-attainment status. IV estimates from GMM. Robust standard errors,adjusted for clustering at the state-level, in parentheses. ∗, ∗∗, and ∗∗∗ denote estimates different fromzero at the 10%, 5%, and 1% significance levels.

54

Table B.4: The effect of decentralization on the count of county-level 2-digit SIC regulatedfirms

(1) (2) (3) (4)

Panel A: OLS estimates

Decentralization 0.2373 0.0422∗∗ 0.0071 0.0060(0.1418) (0.0183) (0.0163) (0.0129)

Competitor decentralization -0.0233 0.0123∗∗ -0.0096 -0.0009(0.0331) (0.0053) (0.0072) (0.0039)

R2 0.0040 0.0010 0.0019 0.8474

Panel B: IV estimates

Decentralization 2.4353 0.0674 0.4381∗ 0.1188(1.5379) (0.0746) (0.2385) (0.0826)

Competitor decentralization -0.1746∗∗ 0.0178 0.0083 -0.0054(0.0711) (0.0146) (0.0376) (0.0162)

F statistic 165.6225 55.7443 49.6681 49.4282Overid. p-value 0.0923 0.5038 0.9944 0.6356

County FE N Y Y YYear FE N N Y YEconomic controls N N N YObservations 49736 49736 49736 40970

Notes: The dependant variable is the logarithm of the number of 2-digit SIC establishments in a county.Economic controls include the logarithm of state GDP and population, for the state, competitor states,and upwind states, as well as county-level logarithm of real income per capita and population, andNAAQS partial or whole non-attainment status. IV estimates from GMM. Robust standard errors,adjusted for clustering at the state-level, in parentheses. ∗, ∗∗, and ∗∗∗ denote estimates different fromzero at the 10%, 5%, and 1% significance levels.

55

Table B.5: The effect of decentralization on county-level TSP concentrations

(1) (2) (3) (4) (5)

Decentralization -0.1319∗∗∗ -0.1373∗∗∗ -0.0058 0.0003 -0.0123(0.0399) (0.0238) (0.0194) (0.0204) (0.0270)

Log(firm count) 0.1095∗∗∗ 0.1415∗∗∗ 0.1751∗∗∗ 0.1970∗∗∗ 0.0901∗∗

(0.0343) (0.0357) (0.0484) (0.0566) (0.0399)Upwind decentralization -0.1026∗∗∗ -0.0973∗∗∗ -0.0268 -0.0317∗ -0.0299

(0.0355) (0.0204) (0.0173) (0.0187) (0.0230)Upwind log(firm count) 0.0144∗ 0.0072∗∗ 0.0002 0.0056 -0.0132

(0.0075) (0.0034) (0.0025) (0.0040) (0.0111)R2 0.0775 0.0725 0.0943 0.1039 0.1066

County FE N Y Y Y YYear FE N N Y Y YWeather controls N N N Y YEconomic controls N N N N YObservations 17539 17539 17539 16534 12994

Notes: The dependant variable is the logarithm of county-level annual TSP concentration (µg/m3).Weather controls include surface temperature and temperature difference, for the state and the upwindstates, as well as at the county-level. Economic controls include the logarithm of state GDP andpopulation, for the state, competitor states, and upwind states, as well as county-level logarithm of realincome per capita and population, and NAAQS partial or whole non-attainment status. IV estimatesfrom GMM. Robust standard errors, adjusted for clustering at the state-level, in parentheses. ∗, ∗∗,and ∗∗∗ denote estimates different from zero at the 10%, 5%, and 1% significance levels.

56

Tab

leB

.6:

Est

imat

esof

the

effec

tof

dec

entr

aliz

aton

onco

unty

-lev

el4-

dig

itSIC

count

ofes

tablish

men

tsfo

rdiff

eren

tsp

ecifi

cati

ons

ofco

mp

etit

ion

and

pol

luti

onsp

illo

vers

(a)

Var

yin

gth

edis

tance

for

com

pet

itio

n

Com

pet

itio

nfa

lls

wit

hin

500

km

1000

km

1500

km

2000km

OL

SIV

OL

SIV

OL

SIV

OL

SIV

Dec

entr

aliz

atio

n0.

0164

-0.0

406

0.0

177

0.1

945∗∗

∗0.0

240∗∗

0.1

061∗

∗0.0

179

-0.0

412

(0.0

120)

(0.0

390)

(0.0

121)

(0.0

467)

(0.0

120)

(0.0

449)

(0.0

119)

(0.0

907)

Com

pet

itor

dec

entr

aliz

atio

n-0

.000

4-0

.0200∗

-0.0

039

-0.0

182∗

-0.0

039∗

-0.0

193∗∗

-0.0

050∗

-0.0

178

(0.0

062)

(0.0

109)

(0.0

029)

(0.0

099)

(0.0

022)

(0.0

083)

(0.0

027)

(0.0

172)

Not

es:

Th

ed

epen

dan

tva

riab

leis

the

loga

rith

mof

the

nu

mb

erof

4-d

igit

SIC

esta

bli

shm

ents

ina

cou

nty

an

dye

ar.

All

spec

ifica

tion

sin

clu

de

stat

ean

dye

arfi

xed

effec

tsan

dth

eli

stof

contr

ols

inT

ab

le5.

∗ ,∗∗

,an

d∗∗

∗d

enote

esti

mate

sd

iffer

ent

from

zero

at

the

10%

,5%

,an

d1%

sign

ifica

nce

level

s.

(b)

Var

yin

gth

edis

tan

cefo

rtr

ansb

oun

dar

yp

ollu

tion

Tra

nsb

oun

dar

yp

ollu

tion

fall

sw

ith

in500

km

1000

km

1500

km

2000km

OL

SIV

OL

SIV

OL

SIV

OL

SIV

Dec

entr

aliz

atio

n0.

0164

-0.0

427

0.0

177

0.0

298

0.0

240∗∗

0.0

135

0.0

179

0.0

212

(0.0

120)

(0.0

761)

(0.0

121)

(0.0

820)

(0.0

120)

(0.1

443)

(0.0

119)

(0.1

073)

Com

pet

itor

dec

entr

aliz

atio

n-0

.000

4-0

.0163

-0.0

039

-0.0

069

-0.0

039∗

-0.0

052

-0.0

050∗

-0.0

048

(0.0

062)

(0.0

287)

(0.0

029)

(0.0

091)

(0.0

022)

(0.0

139)

(0.0

027)

(0.0

165)

Not

es:

Th

ed

epen

dan

tva

riab

leis

the

loga

rith

mof

the

nu

mb

erof

4-d

igit

SIC

esta

bli

shm

ents

ina

cou

nty

an

dye

ar.

All

spec

ifica

tion

sin

clu

de

stat

ean

dye

arfi

xed

effec

tsan

dth

eli

stof

contr

ols

inT

ab

le5.

∗ ,∗∗

,an

d∗∗

∗d

enote

esti

mate

sd

iffer

ent

from

zero

at

the

10%

,5%

,an

d1%

sign

ifica

nce

level

s.

57

Table B.7: Estimates of the effect of decentralization on county-level TSP concentrations,by different distances for transboundary pollution

Transboundary pollution falls within 500 km 1000 km 1500 km 2000 km

Decentralization -0.0143 -0.0222 -0.0224 -0.0250(0.0264) (0.0263) (0.0261) (0.0261)

Log(firm count) 0.0858∗∗ 0.0812∗∗ 0.0788∗∗ 0.0708∗∗

(0.0395) (0.0380) (0.0370) (0.0348)Upwind decentralization -0.0278 -0.0268∗∗ -0.0156∗∗ -0.0122∗∗

(0.0302) (0.0113) (0.0064) (0.0048)Upwind log(firm count) -0.0257∗∗ 0.0044 0.0033 0.0029

(0.0111) (0.0060) (0.0049) (0.0034)

Notes: The dependant variable is the logarithm of the ambient air TSP concentrations in a county andyear. All specifications include state and year fixed effects and the list of controls in Table 6. ∗, ∗∗, and∗∗∗ denote estimates different from zero at the 10%, 5%, and 1% significance levels.

58